diff --git a/CHANGELOG.md b/CHANGELOG.md index 7672ec829..758eae303 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -7,6 +7,21 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## [Unreleased] +### Added +- **`ChaisemartinDHaultfoeuille`** (alias `DCDH`) — Phase 1 of the de Chaisemartin-D'Haultfœuille estimator family, the only modern staggered DiD estimator in the library that handles **non-absorbing (reversible) treatments**. Treatment can switch on AND off over time (marketing campaigns, seasonal promotions, on/off policy cycles). Implements `DID_M` from de Chaisemartin & D'Haultfœuille (2020) AER, equivalently `DID_1` (horizon `l = 1`) of the dynamic companion paper (NBER WP 29873). Ships: + - Headline `DID_M` point estimate with cohort-recentered analytical SE from Web Appendix Section 3.7.3 of the dynamic companion paper + - Joiners-only (`DID_+`) and leavers-only (`DID_-`) decompositions with their own inference + - Single-lag placebo `DID_M^pl` point estimate (AER 2020 placebo specification). Placebo SE / inference fields are intentionally `NaN` in Phase 1: the dynamic companion paper Section 3.7.3 derives the cohort-recentered analytical variance for `DID_l` only, not for the placebo. Phase 2 will add multiplier-bootstrap support for the placebo. The bootstrap path in Phase 1 covers `DID_M`, `DID_+`, and `DID_-` only. + - Optional multiplier bootstrap clustered at group level with Rademacher / Mammen / Webb weights for `DID_M`, `DID_+`, and `DID_-` (placebo bootstrap deferred to Phase 2) + - TWFE decomposition diagnostic from Theorem 1 of AER 2020 (per-cell weights, fraction negative, `sigma_fe`, `beta_fe`) + - Multi-switch group filtering (`drop_larger_lower=True` default, matches R `DIDmultiplegtDYN`); singleton-baseline filter (footnote 15 of dynamic paper, variance computation only); consolidated A11 zero-retention warnings — all with explicit warnings (no silent failures). Never-switching groups participate in the variance via stable-control roles after the Round 2 full-IF fix; the `n_groups_dropped_never_switching` field is retained as backwards-compatibility metadata only. + - Phase 1 requires balanced-baseline panels with no interior period gaps. Late-entry groups (missing the first global period) raise `ValueError`; interior-gap groups are dropped with a `UserWarning`; terminally-missing groups (early exit / right-censoring) are retained and contribute from their observed periods only. This is a documented deviation from R `DIDmultiplegtDYN`'s unbalanced-panel support — see `docs/methodology/REGISTRY.md` for rationale and workarounds. + - Forward-compatible `fit()` signature: Phase 2 (multi-horizon event study, `aggregate`, `L_max`) and Phase 3 (covariate adjustment via `controls`, group-specific linear trends, HonestDiD) parameters present from day one, raising `NotImplementedError` with phase pointers + - Validated against R `DIDmultiplegtDYN` v2.3.3 at horizon `l = 1` via `tests/test_chaisemartin_dhaultfoeuille_parity.py` +- **`twowayfeweights()`** — standalone helper function for the TWFE decomposition diagnostic (Theorem 1 of de Chaisemartin & D'Haultfœuille 2020), available without instantiating the full estimator. Returns a `TWFEWeightsResult` with per-cell weights, fraction negative, `sigma_fe`, and `beta_fe`. +- **`generate_reversible_did_data()`** — new generator in `diff_diff.prep` producing reversible-treatment panel data for testing and tutorials. Patterns: `single_switch` (default, A5-safe), `joiners_only`, `leavers_only`, `mixed_single_switch`, `random`, `cycles`, `marketing`. Returns columns `group`, `period`, `treatment`, `outcome`, `true_effect`, `d_lag`, `switcher_type`. +- **REGISTRY.md `## ChaisemartinDHaultfoeuille` section** — single canonical source for dCDH methodology, equations, edge cases, and all documented deviations from the R `DIDmultiplegtDYN` reference implementation. Cites the AER 2020 paper and the dynamic companion paper (NBER WP 29873) by reference; primary papers are upstream sources, not in-repo files. + ## [3.0.1] - 2026-04-07 ### Added diff --git a/README.md b/README.md index 5159a8554..5122daa6a 100644 --- a/README.md +++ b/README.md @@ -96,6 +96,7 @@ Already know DiD? The [academic quickstart](docs/quickstart.rst) and [estimator - **Panel data support**: Two-way fixed effects estimator for panel designs - **Multi-period analysis**: Event-study style DiD with period-specific treatment effects - **Staggered adoption**: Callaway-Sant'Anna (2021), Sun-Abraham (2021), Borusyak-Jaravel-Spiess (2024) imputation, Two-Stage DiD (Gardner 2022), Stacked DiD (Wing, Freedman & Hollingsworth 2024), Efficient DiD (Chen, Sant'Anna & Xie 2025), and Wooldridge ETWFE (2021/2023) estimators for heterogeneous treatment timing +- **Reversible (non-absorbing) treatments**: de Chaisemartin-D'Haultfœuille `DID_M` estimator for treatments that switch on AND off over time (marketing campaigns, seasonal promotions, on/off policy cycles) — the only library option for non-absorbing treatments - **Triple Difference (DDD)**: Ortiz-Villavicencio & Sant'Anna (2025) estimators with proper covariate handling - **Synthetic DiD**: Combined DiD with synthetic control for improved robustness - **Triply Robust Panel (TROP)**: Factor-adjusted DiD with synthetic weights (Athey et al. 2025) @@ -130,6 +131,7 @@ All estimators have short aliases for convenience: | `Bacon` | `BaconDecomposition` | Goodman-Bacon decomposition | | `EDiD` | `EfficientDiD` | Efficient DiD | | `ETWFE` | `WooldridgeDiD` | Wooldridge ETWFE (2021/2023) | +| `DCDH` | `ChaisemartinDHaultfoeuille` | de Chaisemartin & D'Haultfœuille (2020) — reversible treatments | `TROP` already uses its short canonical name and needs no alias. @@ -1151,6 +1153,87 @@ EfficientDiD( | Covariates | Not yet (Phase 2) | Supported (OR, IPW, DR) | | When to choose | Maximum efficiency, PT-All credible | Covariates needed, weaker PT | +### de Chaisemartin-D'Haultfœuille (dCDH) for Reversible Treatments + +`ChaisemartinDHaultfoeuille` (alias `DCDH`) is the only library estimator that handles **non-absorbing (reversible) treatments** — treatment can switch on AND off over time. This is the natural fit for marketing campaigns, seasonal promotions, on/off policy cycles. + +Phase 1 ships the contemporaneous-switch estimator `DID_M` from the AER 2020 paper, which is mathematically identical to `DID_1` (horizon `l = 1`) of the dynamic companion paper (NBER WP 29873). Phase 2 will add multi-horizon event-study output `DID_l` for `l > 1` on the same class; Phase 3 will add covariate adjustment. + +```python +from diff_diff import ChaisemartinDHaultfoeuille +from diff_diff.prep import generate_reversible_did_data + +# Generate a reversible-treatment panel +data = generate_reversible_did_data( + n_groups=80, n_periods=6, pattern="single_switch", seed=42, +) + +# Fit the estimator +est = ChaisemartinDHaultfoeuille() +results = est.fit( + data, + outcome="outcome", + group="group", + time="period", + treatment="treatment", +) +results.print_summary() + +# Decomposition +print(f"DID_M (overall): {results.overall_att:.3f}") +print(f"DID_+ (joiners): {results.joiners_att:.3f}") +print(f"DID_- (leavers): {results.leavers_att:.3f}") +print(f"Placebo (DID^pl): {results.placebo_effect:.3f}") +``` + +**Parameters:** + +```python +ChaisemartinDHaultfoeuille( + alpha=0.05, # Significance level + n_bootstrap=0, # 0 = analytical SE only; >0 = multiplier bootstrap + bootstrap_weights="rademacher", # 'rademacher', 'mammen', or 'webb' + seed=None, # Random seed for bootstrap + placebo=True, # Auto-compute single-lag placebo + twfe_diagnostic=True, # Auto-compute TWFE decomposition diagnostic + drop_larger_lower=True, # Drop multi-switch groups (matches R DIDmultiplegtDYN) + rank_deficient_action="warn", # Used by TWFE diagnostic OLS +) +``` + +**What you get back on the results object:** + +| Field | Description | +|-------|-------------| +| `overall_att`, `overall_se`, `overall_conf_int` | `DID_M` and inference (cohort-recentered analytical SE by default; multiplier-bootstrap percentile inference when `n_bootstrap > 0`) | +| `joiners_att`, `leavers_att` | Decomposition into the joiners (`DID_+`) and leavers (`DID_-`) views | +| `placebo_effect` | Single-lag placebo (`DID_M^pl`) point estimate | +| `per_period_effects` | Per-period decomposition with explicit A11-violation flags | +| `twfe_weights`, `twfe_fraction_negative`, `twfe_sigma_fe`, `twfe_beta_fe` | Theorem 1 decomposition diagnostic | +| `n_groups_dropped_crossers`, `n_groups_dropped_singleton_baseline` | Filter counts (multi-switch groups dropped before estimation; singleton-baseline groups excluded from variance) | +| `n_groups_dropped_never_switching` | Backwards-compatibility metadata. Never-switching groups participate in the variance via stable-control roles; this field is no longer a filter count. | + +**Standalone TWFE decomposition diagnostic** (without fitting the full estimator): + +```python +from diff_diff import twowayfeweights + +diagnostic = twowayfeweights( + data, outcome="outcome", group="group", time="period", treatment="treatment", +) +print(f"Plain TWFE coefficient: {diagnostic.beta_fe:.3f}") +print(f"Fraction of negative weights: {diagnostic.fraction_negative:.3f}") +print(f"sigma_fe (sign-flipping threshold): {diagnostic.sigma_fe:.3f}") +``` + +> **Note:** The Phase 1 placebo SE is intentionally `NaN` with a warning. The dynamic companion paper Section 3.7.3 derives the cohort-recentered analytical variance for `DID_l` only — not for the placebo `DID_M^pl`. Phase 2 will add multiplier-bootstrap support for the placebo via the dynamic paper's machinery. Until then, the placebo point estimate is meaningful but its inference fields are NaN-consistent (and `results.placebo_se`, `results.placebo_p_value`, etc. remain `NaN` even when `n_bootstrap > 0`). + +> **Note:** By default (`drop_larger_lower=True`), the estimator drops groups whose treatment switches more than once before estimation. This matches R `DIDmultiplegtDYN`'s default and is required for the analytical variance formula to be consistent with the point estimate. Each drop emits an explicit warning. + +> **Note:** Phase 1 requires panels with a **balanced baseline** (every group observed at the first global period) and **no interior period gaps**. Late-entry groups (missing the baseline) raise `ValueError`; interior-gap groups are dropped with a warning; terminally-missing groups (early exit / right-censoring) are retained and contribute from their observed periods only. This is a documented deviation from R `DIDmultiplegtDYN`, which supports unbalanced panels — see [`docs/methodology/REGISTRY.md`](docs/methodology/REGISTRY.md) for the rationale, the defensive guards that make terminal missingness safe, and workarounds for unbalanced inputs. + +> **Note:** Survey design (`survey_design`), event-study aggregation (`aggregate`), covariate adjustment (`controls`), and HonestDiD integration (`honest_did`) are not yet supported. They raise `NotImplementedError` with phase pointers — see [`ROADMAP.md`](ROADMAP.md) for the full multi-phase rollout. + ### Triple Difference (DDD) Triple Difference (DDD) is used when treatment requires satisfying two criteria: belonging to a treated **group** AND being in an eligible **partition**. The `TripleDifference` class implements the methodology from Ortiz-Villavicencio & Sant'Anna (2025), which correctly handles covariate adjustment (unlike naive implementations). diff --git a/ROADMAP.md b/ROADMAP.md index 4a0b7ab49..59035c5a4 100644 --- a/ROADMAP.md +++ b/ROADMAP.md @@ -116,17 +116,90 @@ Parallel track targeting data science practitioners — marketing, product, oper --- -## Future Estimators +## de Chaisemartin-D'Haultfœuille (dCDH) Estimator + +The dCDH estimator is the only modern DiD estimator in the library that handles **non-absorbing (reversible) treatments**. All other staggered estimators (CallawaySantAnna, SunAbraham, ImputationDiD, TwoStageDiD, EfficientDiD, WooldridgeDiD) assume treatment is an absorbing state — once treated, always treated. dCDH is the natural fit for marketing campaigns, seasonal promotions, policy on/off cycles, and any setting where treatment turns on and off over time. + +**Implementation strategy.** A single `ChaisemartinDHaultfoeuille` (alias `DCDH`) class evolves across phases via additional `fit()` parameters and additional fields on the results object. Not an estimator family — features land as enhancements to the single class, matching the library's pattern for `CallawaySantAnna`, `ImputationDiD`, `EfficientDiD`, etc. + +**Methodology source of truth:** [docs/methodology/REGISTRY.md `## ChaisemartinDHaultfoeuille`](docs/methodology/REGISTRY.md) — assumption checks, estimator equations, edge cases, and all documented deviations from the R `DIDmultiplegtDYN` reference implementation. Consult REGISTRY.md before any methodology change. + +**Primary papers** (consulted by the implementer; not committed in-repo as they are upstream sources): +- de Chaisemartin, C. & D'Haultfœuille, X. (2020). Two-Way Fixed Effects Estimators with Heterogeneous Treatment Effects. *American Economic Review*, 110(9), 2964-2996. — `DID_M` contemporaneous-switch estimator, TWFE decomposition diagnostics. +- de Chaisemartin, C. & D'Haultfœuille, X. (2022, revised 2024). Difference-in-Differences Estimators of Intertemporal Treatment Effects. NBER Working Paper 29873. — Full dynamic event study `DID_l`, cohort-recentered analytical variance (Web Appendix Section 3.7.3), residualization-style covariates `DID^X`, group-specific linear trends `DID^{fd}`. + +The dynamic companion paper subsumes the AER 2020 paper: `DID_1 = DID_M`. The single class implements the dynamic estimator's machinery (`DID_{g,l}` building block, cohort-recentered analytical variance from Web Appendix Section 3.7.3 of the dynamic paper) at horizon `l = 1` for Phase 1, with later phases looping over multiple horizons and adding covariate / extension support. + +### Phase 1: Foundation (contemporaneous switch / DID_M) -### de Chaisemartin-D'Haultfouille Estimator +*Goal: Ship a `ChaisemartinDHaultfoeuille` estimator class for the contemporaneous-switch case (`l = 1`), which is `DID_M` of the AER 2020 paper. Forward-compatible API: parameters and result fields for Phase 2/3 are reserved from day one and raise `NotImplementedError` with phase pointers until they're implemented.* -Handles treatment that switches on and off (reversible treatments), unlike most other methods. Reversible treatments are common in marketing (seasonal campaigns, promotions), giving this estimator higher priority for data science practitioners. +| Item | Priority | Status | +|------|----------|--------| +| **1a.** `ChaisemartinDHaultfoeuille` class with `fit()` returning per-group `DID_{g,1}` and aggregate `DID_1` / `DID_M` | HIGH | Shipped | +| **1b.** Joiners-only (`DID_+`) and leavers-only (`DID_-`) views on the results object | HIGH | Shipped | +| **1c.** Single-lag placebo `DID_M^pl` (AER 2020 placebo specification = `DID^{pl}_1` of dynamic paper) | HIGH | Shipped (point estimate; analytical SE deferred to Phase 2) | +| **1d.** Analytical SE via cohort-recentered plug-in formula (Web Appendix Section 3.7.3 of dynamic paper, applied at `l = 1`) | HIGH | Shipped | +| **1e.** Multiplier bootstrap clustered at the group level (library extension; matches CS / ImputationDiD / TwoStageDiD convention) | HIGH | Shipped | +| **1f.** TWFE decomposition diagnostic: per-`(g, t)` weights, fraction negative, `sigma_fe` (Theorem 1 of AER 2020 + `twowayfeweights` parity) | MEDIUM | Shipped | +| **1g.** Parity tests vs R `DIDmultiplegtDYN` at `l = 1` | HIGH | Shipped | +| **1h.** REGISTRY.md entry, doc-deps.yaml mapping, README.md section, RST docs, CHANGELOG.md entry | HIGH | Shipped | +| **1i.** Survey compatibility matrix in `docs/choosing_estimator.rst`: explicitly document **NO survey support** for dCDH (separate effort after all phases ship) | HIGH | Shipped | + +### Phase 2: Dynamic event study (multiple horizons) + +*Goal: Add `aggregate="event_study"` mode to the same class. Loops the Phase 1 machinery over horizons `l = 1, ..., L`. No API breakage from Phase 1. No new tutorial — the comprehensive tutorial waits for Phase 3.* + +| Item | Priority | Status | +|------|----------|--------| +| **2a.** Multi-horizon `DID_l` via the cohort framework, with horizon parameter `L_max` | HIGH | Not started | +| **2b.** Multi-horizon analytical SE (same plug-in formula looped over horizons) | HIGH | Not started | +| **2c.** Dynamic placebos `DID^{pl}_l` for pre-trends testing (Web Appendix Section 1.1 of dynamic paper) | HIGH | Not started | +| **2d.** Normalized estimator `DID^n_l` (Section 3.2 of dynamic paper) | MEDIUM | Not started | +| **2e.** Cost-benefit aggregate `delta` (Section 3.3 of dynamic paper, Lemma 4) | MEDIUM | Not started | +| **2f.** Simultaneous (sup-t) confidence bands for event study plots | MEDIUM | Not started | +| **2g.** `plot_event_study()` integration; `< 50%`-of-switchers warning for far horizons | MEDIUM | Not started | +| **2h.** Parity tests vs `did_multiplegt_dyn` for multi-horizon designs | HIGH | Not started | -- Allows units to move into and out of treatment -- Time-varying, heterogeneous treatment effects -- Comparison with never-switchers or flexible control groups +### Phase 3: Covariates, extensions, and tutorial -**Reference**: [de Chaisemartin & D'Haultfouille (2020, 2024)](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3980758). *American Economic Review*. +*Goal: Add residualization-style covariate adjustment, group-specific linear trends, non-binary treatment support, HonestDiD integration, and a single comprehensive tutorial covering all three phases. This is the phase where dCDH ships as a complete public feature.* + +| Item | Priority | Status | +|------|----------|--------| +| **3a.** Residualization-style covariate adjustment `DID^X` (Web Appendix Section 1.2 of dynamic paper). **Note:** NOT doubly-robust, NOT IPW, NOT Callaway-Sant'Anna-style. | HIGH | Not started | +| **3b.** Group-specific linear trends `DID^{fd}` (Web Appendix Section 1.3, Lemma 6) — second-difference estimator with cumulation for level effects | MEDIUM | Not started | +| **3c.** State-set-specific trends (`trends_nonparam` option, Web Appendix Section 1.4) | MEDIUM | Not started | +| **3d.** Heterogeneity testing `beta^{het}_l` (Web Appendix Section 1.5) | LOW | Not started | +| **3e.** Design-2 switch-in / switch-out separation (Web Appendix Section 1.6) | LOW | Not started | +| **3f.** Non-binary treatment support (the formula already handles it; this row is documentation + tests) | MEDIUM | Not started | +| **3g.** HonestDiD (Rambachan-Roth) integration on `DID^{pl}_l` placebos | MEDIUM | Not started | +| **3h.** **Single comprehensive tutorial notebook** covering all three phases — Favara-Imbs (2015) banking deregulation replication as the headline application, with comparison plots vs LP / TWFE | HIGH | Not started | +| **3i.** Parity tests vs `did_multiplegt_dyn` for covariate and extension specifications | HIGH | Not started | + +### Out of scope for the dCDH single-class evolution + +These are referenced by the dCDH papers but live in *separate* efforts or *separate* companion papers we don't yet have: + +- **Survey design integration** — deferred to a separate effort after all three phases ship. Phase 1 documents "no survey support" in the compatibility matrix; the separate effort revisits when Phase 3 is complete. +- **Fuzzy DiD** (within-cell-varying treatment, Web Appendix Section 1.7 of dynamic paper) → de Chaisemartin & D'Haultfœuille (2018), separate paper not yet reviewed +- **Principled anticipation handling and trimming rules** (footnote 14 of dynamic paper) → de Chaisemartin (2021), separate paper not yet reviewed +- **2SLS DiD** (referenced in AER appendix Section 3.4) → separate paper + +These remain in **Future Estimators** below if/when we choose to extend. + +### Architectural notes (for plan and PR reviewers) + +- **Single `ChaisemartinDHaultfoeuille` class** (alias `DCDH`). Not a family. New features land as `fit()` parameters or fields on the results dataclass. No `DCDHDynamic`, `DCDHCovariate`, etc. Matches the library's idiomatic pattern: `CallawaySantAnna`, `ImputationDiD`, and `EfficientDiD` are all single classes that evolved across many phases. +- **Forward-compatible API from Phase 1.** `fit(aggregate=None, controls=None, trends_linear=None, L_max=None, ...)` accepts the Phase 2/3 parameters from day one and raises `NotImplementedError` with a clear pointer to the relevant phase until they are implemented. No signature changes between phases. +- **Conservative CI** under Assumption 8 (independent groups), exact only under iid sampling. Documented in REGISTRY.md as a `**Note:**` deviation from "default nominal coverage." Theorem 1 of the dynamic paper. +- **Cohort recentering for variance is essential.** Cohorts are defined by the triple `(D_{g,1}, F_g, S_g)`. The plug-in variance subtracts cohort-conditional means, **NOT a single grand mean**. Test fixtures must catch this — a wrong implementation silently produces a smaller, incorrect variance. +- **No Rust acceleration is planned for any phase.** The estimator's hot path is groupby + BLAS-accelerated matrix-vector products, where NumPy already operates near-optimally. If profiling on large panels (`G > 100K`) reveals a bottleneck post-ship, the existing `_rust_bootstrap_weights` helper can be reused for the bootstrap loop without writing new Rust code. +- **No survey design integration in any phase.** Handled as a separate effort after all three phases ship. Phase 1 documents the absence in the compatibility matrix so survey users do not silently apply survey weights and get wrong answers. + +--- + +## Future Estimators ### Local Projections DiD diff --git a/benchmarks/R/generate_dcdh_dynr_test_values.R b/benchmarks/R/generate_dcdh_dynr_test_values.R new file mode 100644 index 000000000..8bbe76fc3 --- /dev/null +++ b/benchmarks/R/generate_dcdh_dynr_test_values.R @@ -0,0 +1,307 @@ +#!/usr/bin/env Rscript +# Generate golden values for de Chaisemartin-D'Haultfoeuille (dCDH) +# parity tests at horizon l=1. +# +# This script fits the R `DIDmultiplegtDYN` package's `did_multiplegt_dyn` +# function (the official R implementation of the dCDH dynamic-effects +# companion paper, NBER WP 29873) on a set of canonical reversible-treatment +# scenarios. At horizon l=1, did_multiplegt_dyn computes DID_1, which is +# numerically identical to DID_M of the AER 2020 paper. Phase 1 of the +# Python diff-diff dCDH implementation tests for parity against these +# golden values. +# +# Usage: +# Rscript benchmarks/R/generate_dcdh_dynr_test_values.R +# +# Prerequisites: +# install.packages("DIDmultiplegtDYN") # CRAN v2.3.3+ +# install.packages("jsonlite") +# +# Output: +# benchmarks/data/dcdh_dynr_golden_values.json +# +# Each scenario exports: +# - data: the simulated dataset (so Python tests use identical data) +# - params: the dCDH options used +# - results: DID_1 point estimate, SE, CI, placebo, switcher counts + +library(DIDmultiplegtDYN) +library(jsonlite) +suppressMessages(library(polars)) # required by DIDmultiplegtDYN >= 2.x + +cat("Generating dCDH golden values via DIDmultiplegtDYN at l=1...\n") + +output_path <- file.path("benchmarks", "data", "dcdh_dynr_golden_values.json") + +# --------------------------------------------------------------------------- +# Helper: Python-mirror reversible-treatment generator. +# Mirrors generate_reversible_did_data() in diff_diff/prep_dgp.py at the +# STRUCTURAL level — the two implementations apply the same pattern logic +# (single_switch / joiners_only / leavers_only / mixed_single_switch) and +# the same fixed-effect / treatment-effect / time-trend / noise model. They +# do NOT produce bit-identical draws even with the same seed: R's set.seed +# and NumPy's default_rng use different RNGs and the parity tests don't +# rely on RNG identity. Instead, the parity tests load THIS R script's +# golden-value JSON output and pass the SAME data (group/period/treatment/ +# outcome columns) to the Python estimator, so both sides operate on +# byte-identical input regardless of how it was originally generated. +# --------------------------------------------------------------------------- +gen_reversible <- function(n_groups, n_periods, pattern, seed, + p_switch = 0.2, initial_treat_frac = 0.3, + cycle_length = 2, treatment_effect = 2.0, + heterogeneous_effects = FALSE, effect_sd = 0.5, + group_fe_sd = 2.0, time_trend = 0.1, noise_sd = 0.5, + n_never_treated = 20, n_always_treated = 20) { + # n_never_treated and n_always_treated add stable control cohorts so + # both Python (AER 2020 zero-retention) and R DIDmultiplegtDYN (dynamic + # paper, drop-cohort) implementations have controls available at every + # period — eliminating the methodology divergence and giving a clean + # parity comparison. The total returned panel has + # n_groups + n_never_treated + n_always_treated groups. + set.seed(seed) + + # --- Build the (n_groups, n_periods) treatment matrix --- + D <- matrix(0L, nrow = n_groups, ncol = n_periods) + + if (pattern == "single_switch") { + initial_treated <- runif(n_groups) < initial_treat_frac + switch_times <- sample.int(n_periods - 1, n_groups, replace = TRUE) # 1..(n_periods-1) + for (g in seq_len(n_groups)) { + st <- switch_times[g] + 1L # convert to 1-indexed switch period + if (initial_treated[g]) { + D[g, seq_len(st - 1L)] <- 1L + D[g, st:n_periods] <- 0L + } else { + D[g, seq_len(st - 1L)] <- 0L + D[g, st:n_periods] <- 1L + } + } + } else if (pattern == "joiners_only") { + switch_times <- sample.int(n_periods - 1, n_groups, replace = TRUE) + for (g in seq_len(n_groups)) { + st <- switch_times[g] + 1L + D[g, st:n_periods] <- 1L + } + } else if (pattern == "leavers_only") { + switch_times <- sample.int(n_periods - 1, n_groups, replace = TRUE) + for (g in seq_len(n_groups)) { + st <- switch_times[g] + 1L + D[g, seq_len(st - 1L)] <- 1L + } + } else if (pattern == "mixed_single_switch") { + switch_times <- sample.int(n_periods - 1, n_groups, replace = TRUE) + n_joiners <- n_groups %/% 2 + for (g in seq_len(n_groups)) { + st <- switch_times[g] + 1L + if (g <= n_joiners) { + D[g, st:n_periods] <- 1L + } else { + D[g, seq_len(st - 1L)] <- 1L + } + } + } else { + stop(sprintf("Unknown pattern: %s", pattern)) + } + + # --- Append stable control cohorts --- + if (n_never_treated > 0) { + D <- rbind(D, matrix(0L, nrow = n_never_treated, ncol = n_periods)) + } + if (n_always_treated > 0) { + D <- rbind(D, matrix(1L, nrow = n_always_treated, ncol = n_periods)) + } + n_groups <- nrow(D) + + # --- Generate fixed effects, true effects, outcomes --- + group_fe <- rnorm(n_groups, mean = 0, sd = group_fe_sd) + if (heterogeneous_effects) { + true_effects <- matrix(rnorm(n_groups * n_periods, mean = treatment_effect, sd = effect_sd), + nrow = n_groups, ncol = n_periods) + } else { + true_effects <- matrix(treatment_effect, nrow = n_groups, ncol = n_periods) + } + true_effects[D == 0] <- 0.0 + + period_arr <- 0:(n_periods - 1) + noise <- matrix(rnorm(n_groups * n_periods, mean = 0, sd = noise_sd), + nrow = n_groups, ncol = n_periods) + Y <- 10.0 + matrix(group_fe, nrow = n_groups, ncol = n_periods) + + matrix(time_trend * period_arr, nrow = n_groups, ncol = n_periods, byrow = TRUE) + + true_effects + noise + + # --- Build long-format data frame --- + group_col <- rep(seq_len(n_groups) - 1L, each = n_periods) # 0-indexed + period_col <- rep(period_arr, n_groups) + treatment_col <- as.vector(t(D)) + outcome_col <- as.vector(t(Y)) + + data.frame( + group = group_col, + period = period_col, + treatment = treatment_col, + outcome = outcome_col + ) +} + +# --------------------------------------------------------------------------- +# Helper: extract DID_1 (l=1) results from did_multiplegt_dyn output +# --------------------------------------------------------------------------- +extract_dcdh_l1 <- function(res) { + # did_multiplegt_dyn returns a results object with $results$Effects matrix. + # The Effects matrix has one row per effect (Effect_1, Effect_2, ...) and + # columns: Estimate, SE, LB CI, UB CI, N, Switchers, N.w, Switchers.w. We + # pull the row for "Effect_1" (the l=1 effect, == DID_M of AER 2020). + effects <- res$results$Effects + if (is.null(effects)) { + stop("did_multiplegt_dyn returned no Effects; check the input data") + } + + out <- list( + overall_att = as.numeric(effects[1, "Estimate"]), + overall_se = as.numeric(effects[1, "SE"]), + overall_ci_lo = as.numeric(effects[1, "LB CI"]), + overall_ci_hi = as.numeric(effects[1, "UB CI"]), + n_switchers = as.numeric(effects[1, "N"]) + ) + + # Placebo at lag 1 if available (Placebos has the same column layout) + placebos <- res$results$Placebos + if (!is.null(placebos) && nrow(placebos) >= 1) { + out$placebo_effect <- as.numeric(placebos[1, "Estimate"]) + out$placebo_se <- as.numeric(placebos[1, "SE"]) + out$placebo_ci_lo <- as.numeric(placebos[1, "LB CI"]) + out$placebo_ci_hi <- as.numeric(placebos[1, "UB CI"]) + } + + out +} + +# --------------------------------------------------------------------------- +# Helper: convert data frame to exportable list +# --------------------------------------------------------------------------- +export_data <- function(df) { + list( + group = as.numeric(df$group), + period = as.numeric(df$period), + treatment = as.numeric(df$treatment), + outcome = as.numeric(df$outcome) + ) +} + +scenarios <- list() + +# Golden value datasets use n_groups=80 to keep the JSON file small (~50KB) +# while still being large enough to exercise inference. +N_GOLDEN <- 80 + +# --------------------------------------------------------------------------- +# Scenario 1: single_switch — mix of joiners and leavers +# --------------------------------------------------------------------------- +cat(" Scenario 1: single_switch_mixed\n") +d1 <- gen_reversible(n_groups = N_GOLDEN, n_periods = 6, + pattern = "single_switch", seed = 101) +res1 <- did_multiplegt_dyn( + df = d1, outcome = "outcome", group = "group", time = "period", + treatment = "treatment", effects = 1, placebo = 1, ci_level = 95 +) +scenarios$single_switch_mixed <- list( + data = export_data(d1), + params = list(pattern = "single_switch", n_groups = N_GOLDEN, n_periods = 6, + seed = 101, effects = 1, placebo = 1, ci_level = 95), + results = extract_dcdh_l1(res1) +) + +# --------------------------------------------------------------------------- +# Scenario 2: joiners_only — pure staggered adoption +# --------------------------------------------------------------------------- +cat(" Scenario 2: joiners_only\n") +d2 <- gen_reversible(n_groups = N_GOLDEN, n_periods = 6, + pattern = "joiners_only", seed = 102) +res2 <- did_multiplegt_dyn( + df = d2, outcome = "outcome", group = "group", time = "period", + treatment = "treatment", effects = 1, placebo = 1, ci_level = 95 +) +scenarios$joiners_only <- list( + data = export_data(d2), + params = list(pattern = "joiners_only", n_groups = N_GOLDEN, n_periods = 6, + seed = 102, effects = 1, placebo = 1, ci_level = 95), + results = extract_dcdh_l1(res2) +) + +# --------------------------------------------------------------------------- +# Scenario 3: leavers_only — pure staggered removal +# --------------------------------------------------------------------------- +cat(" Scenario 3: leavers_only\n") +d3 <- gen_reversible(n_groups = N_GOLDEN, n_periods = 6, + pattern = "leavers_only", seed = 103) +res3 <- did_multiplegt_dyn( + df = d3, outcome = "outcome", group = "group", time = "period", + treatment = "treatment", effects = 1, placebo = 1, ci_level = 95 +) +scenarios$leavers_only <- list( + data = export_data(d3), + params = list(pattern = "leavers_only", n_groups = N_GOLDEN, n_periods = 6, + seed = 103, effects = 1, placebo = 1, ci_level = 95), + results = extract_dcdh_l1(res3) +) + +# --------------------------------------------------------------------------- +# Scenario 4: mixed_single_switch — deterministic 50/50 joiners/leavers +# --------------------------------------------------------------------------- +cat(" Scenario 4: mixed_single_switch\n") +d4 <- gen_reversible(n_groups = N_GOLDEN, n_periods = 6, + pattern = "mixed_single_switch", seed = 104) +res4 <- did_multiplegt_dyn( + df = d4, outcome = "outcome", group = "group", time = "period", + treatment = "treatment", effects = 1, placebo = 1, ci_level = 95 +) +scenarios$mixed_single_switch <- list( + data = export_data(d4), + params = list(pattern = "mixed_single_switch", n_groups = N_GOLDEN, n_periods = 6, + seed = 104, effects = 1, placebo = 1, ci_level = 95), + results = extract_dcdh_l1(res4) +) + +# --------------------------------------------------------------------------- +# Scenario 5: hand-calculable 4-group panel from the worked example. +# This is the panel used by tests/test_methodology_chaisemartin_dhaultfoeuille.py +# in test_hand_calculable_4group_3period_joiners_and_leavers. We capture R's +# answer here so the Python test can assert exact agreement. +# --------------------------------------------------------------------------- +cat(" Scenario 5: hand_calculable_worked_example\n") +d5 <- data.frame( + group = c(1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4), + period = c(0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2), + treatment = c(0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1), + outcome = c(10, 13, 14, 10, 11, 9, 10, 11, 12, 10, 11, 12) +) +res5 <- did_multiplegt_dyn( + df = d5, outcome = "outcome", group = "group", time = "period", + treatment = "treatment", effects = 1, placebo = 0, ci_level = 95 +) +scenarios$hand_calculable_worked_example <- list( + data = export_data(d5), + params = list(description = "4-group hand-calculable panel from plan worked example", + effects = 1, placebo = 0, ci_level = 95, + expected_did_m = 2.5, expected_did_plus = 2.0, expected_did_minus = 3.0), + results = extract_dcdh_l1(res5) +) + +# --------------------------------------------------------------------------- +# Write output +# --------------------------------------------------------------------------- +dir.create(dirname(output_path), showWarnings = FALSE, recursive = TRUE) +writeLines( + toJSON( + list(scenarios = scenarios, + generator = "generate_reversible_did_data v1", + dcdh_package = paste0("DIDmultiplegtDYN ", utils::packageVersion("DIDmultiplegtDYN"))), + auto_unbox = TRUE, + digits = 10, + pretty = TRUE + ), + output_path +) +cat(sprintf("dCDH golden values written to %s\n", output_path)) +cat(sprintf("File size: %.1f KB\n", file.info(output_path)$size / 1024)) +cat(sprintf("Scenarios: %d\n", length(scenarios))) diff --git a/benchmarks/data/dcdh_dynr_golden_values.json b/benchmarks/data/dcdh_dynr_golden_values.json new file mode 100644 index 000000000..faf2ff628 --- /dev/null +++ b/benchmarks/data/dcdh_dynr_golden_values.json @@ -0,0 +1,142 @@ +{ + "scenarios": { + "single_switch_mixed": { + "data": { + 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12.0935915605, 11.112434334] + }, + "params": { + "pattern": "mixed_single_switch", + "n_groups": 80, + "n_periods": 6, + "seed": 104, + "effects": 1, + "placebo": 1, + "ci_level": 95 + }, + "results": { + "overall_att": 1.8863184672, + "overall_se": 0.089476201544, + "overall_ci_lo": 1.7109483347, + "overall_ci_hi": 2.0616885997, + "n_switchers": 457, + "placebo_effect": 0.088980107217, + "placebo_se": 0.096317212144, + "placebo_ci_lo": -0.099798159678, + "placebo_ci_hi": 0.27775837411 + } + }, + "hand_calculable_worked_example": { + "data": { + "group": [1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4], + "period": [0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2], + "treatment": [0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1], + "outcome": [10, 13, 14, 10, 11, 9, 10, 11, 12, 10, 11, 12] + }, + "params": { + "description": "4-group hand-calculable panel from plan worked example", + "effects": 1, + "placebo": 0, + "ci_level": 95, + "expected_did_m": 2.5, + "expected_did_plus": 2, + "expected_did_minus": 3 + }, + "results": { + "overall_att": 2.5, + "overall_se": 1.8027756377, + "overall_ci_lo": -1.0333753222, + "overall_ci_hi": 6.0333753222, + "n_switchers": 4 + } + } + }, + "generator": "generate_reversible_did_data v1", + "dcdh_package": "DIDmultiplegtDYN 2.3.3" +} diff --git a/diff_diff/__init__.py b/diff_diff/__init__.py index 41ec6445e..03a07e6e6 100644 --- a/diff_diff/__init__.py +++ b/diff_diff/__init__.py @@ -88,6 +88,7 @@ generate_event_study_data, generate_factor_data, generate_panel_data, + generate_reversible_did_data, generate_staggered_data, generate_staggered_ddd_data, generate_survey_did_data, @@ -160,6 +161,16 @@ EfficientDiDResults, EDiDBootstrapResults, ) +from diff_diff.chaisemartin_dhaultfoeuille import ( + ChaisemartinDHaultfoeuille, + TWFEWeightsResult, + chaisemartin_dhaultfoeuille, + twowayfeweights, +) +from diff_diff.chaisemartin_dhaultfoeuille_results import ( + ChaisemartinDHaultfoeuilleResults, + DCDHBootstrapResults, +) from diff_diff.trop import ( TROP, TROPResults, @@ -214,6 +225,7 @@ Bacon = BaconDecomposition EDiD = EfficientDiD ETWFE = WooldridgeDiD +DCDH = ChaisemartinDHaultfoeuille __version__ = "3.0.1" __all__ = [ @@ -223,6 +235,7 @@ "MultiPeriodDiD", "SyntheticDiD", "CallawaySantAnna", + "ChaisemartinDHaultfoeuille", "ContinuousDiD", "SunAbraham", "ImputationDiD", @@ -237,6 +250,7 @@ "SDiD", "CS", "CDiD", + "DCDH", "SA", "BJS", "Gardner", @@ -280,6 +294,12 @@ "EfficientDiDResults", "EDiDBootstrapResults", "EDiD", + # ChaisemartinDHaultfoeuille (dCDH) + "ChaisemartinDHaultfoeuilleResults", + "DCDHBootstrapResults", + "TWFEWeightsResult", + "chaisemartin_dhaultfoeuille", + "twowayfeweights", # WooldridgeDiD (ETWFE) "WooldridgeDiD", "WooldridgeDiDResults", @@ -328,6 +348,7 @@ "generate_staggered_ddd_data", "generate_survey_did_data", "generate_continuous_did_data", + "generate_reversible_did_data", "create_event_time", "aggregate_survey", "aggregate_to_cohorts", diff --git a/diff_diff/chaisemartin_dhaultfoeuille.py b/diff_diff/chaisemartin_dhaultfoeuille.py new file mode 100644 index 000000000..8bcac1799 --- /dev/null +++ b/diff_diff/chaisemartin_dhaultfoeuille.py @@ -0,0 +1,2258 @@ +""" +de Chaisemartin-D'Haultfoeuille (dCDH) estimator for reversible-treatment DiD. + +The dCDH estimator is the only modern DiD estimator in the diff-diff library +that handles **non-absorbing (reversible) treatments** — treatment can switch +on AND off over time. All other staggered estimators in the library +(``CallawaySantAnna``, ``SunAbraham``, ``ImputationDiD``, ``TwoStageDiD``, +``EfficientDiD``, ``WooldridgeDiD``) assume treatment is absorbing. + +Phase 1 ships the contemporaneous-switch case ``DID_M`` (= ``DID_1`` at +horizon ``l = 1`` of the dynamic companion paper). Phases 2 and 3 add +dynamic horizons and covariates respectively, on the *same* class — see +``ROADMAP.md`` for the full progression. The forward-compatibility +parameters in :meth:`ChaisemartinDHaultfoeuille.fit` raise +``NotImplementedError`` with phase pointers until later phases land. + +References +---------- +- de Chaisemartin, C. & D'Haultfoeuille, X. (2020). Two-Way Fixed Effects + Estimators with Heterogeneous Treatment Effects. *American Economic + Review*, 110(9), 2964-2996. +- de Chaisemartin, C. & D'Haultfoeuille, X. (2022, revised 2023). + Difference-in-Differences Estimators of Intertemporal Treatment Effects. + NBER Working Paper 29873. Web Appendix Section 3.7.3 contains the + cohort-recentered plug-in variance formula implemented here. +""" + +import warnings +from typing import Any, Dict, List, Optional, Tuple + +import numpy as np +import pandas as pd + +from diff_diff.chaisemartin_dhaultfoeuille_bootstrap import ( + ChaisemartinDHaultfoeuilleBootstrapMixin, +) +from diff_diff.chaisemartin_dhaultfoeuille_results import ( + ChaisemartinDHaultfoeuilleResults, + DCDHBootstrapResults, +) +from diff_diff.linalg import solve_ols +from diff_diff.utils import safe_inference + +__all__ = [ + "ChaisemartinDHaultfoeuille", + "chaisemartin_dhaultfoeuille", + "twowayfeweights", + "TWFEWeightsResult", +] + + +# ============================================================================= +# Public dataclass for the standalone TWFE diagnostic helper +# ============================================================================= + + +class TWFEWeightsResult: + """ + Lightweight container for the standalone ``twowayfeweights`` helper. + + Returned by :func:`twowayfeweights`. Mirrors the per-cell decomposition + information that the dCDH estimator stores on its results object when + ``twfe_diagnostic=True``, but available as a standalone function for + users who only want the diagnostic without fitting the full estimator. + """ + + __slots__ = ("weights", "fraction_negative", "sigma_fe", "beta_fe") + + def __init__( + self, + weights: pd.DataFrame, + fraction_negative: float, + sigma_fe: float, + beta_fe: float, + ) -> None: + self.weights = weights + self.fraction_negative = fraction_negative + self.sigma_fe = sigma_fe + self.beta_fe = beta_fe + + def __repr__(self) -> str: + return ( + f"TWFEWeightsResult(beta_fe={self.beta_fe:.4f}, " + f"fraction_negative={self.fraction_negative:.4f}, " + f"sigma_fe={self.sigma_fe:.4f}, n_cells={len(self.weights)})" + ) + + +# ============================================================================= +# Shared validation + cell aggregation helper +# ============================================================================= + + +def _validate_and_aggregate_to_cells( + data: pd.DataFrame, + outcome: str, + group: str, + time: str, + treatment: str, +) -> pd.DataFrame: + """ + Validate input data and aggregate to ``(g, t)`` cells per the dCDH contract. + + Used by both :meth:`ChaisemartinDHaultfoeuille.fit` and + :func:`twowayfeweights` so the validation rules and aggregation + behavior are identical across the two public entry points. + + The contract (matching ``REGISTRY.md`` ``## ChaisemartinDHaultfoeuille``): + + 1. **Required columns** ``outcome``, ``group``, ``time``, ``treatment`` + must all be present in ``data`` (raises ``ValueError`` listing + any missing). + 2. **Treatment** must coerce to numeric and contain no ``NaN`` + (raises ``ValueError`` — silent dropping would change cell counts + without informing the user). + 3. **Outcome** must coerce to numeric and contain no ``NaN`` (same + reasoning). + 4. **Treatment must be binary** (only ``0`` / ``1`` raw values). + Non-binary treatment is reserved for Phase 3 of the dCDH rollout + and raises ``ValueError``. + 5. **Cell aggregation** via ``groupby([group, time]).agg(...)`` + producing ``y_gt`` (cell mean of ``outcome``), ``d_gt`` (cell + mean of ``treatment``), and ``n_gt`` (count of original + observations in the cell). + 6. **Within-cell-varying treatment** (any cell with fractional + ``d_gt``) raises ``ValueError``. Phase 1 requires treatment to + be constant within each ``(group, time)`` cell; fuzzy DiD is + deferred to a separate dCdH 2018 paper not covered by Phase 1. + Pre-aggregate your data to constant binary cell-level treatment + before calling ``fit()`` or ``twowayfeweights()``. + + Returns the aggregated cell DataFrame with columns + ``[group, time, y_gt, d_gt, n_gt]``, sorted by ``[group, time]`` + with a fresh index. + + Raises + ------ + ValueError + On missing columns, NaN treatment / outcome values, non-numeric + treatment / outcome that cannot be coerced, or non-binary raw + treatment values. + """ + # 1. Required columns + missing = [c for c in (outcome, group, time, treatment) if c not in data.columns] + if missing: + raise ValueError( + f"ChaisemartinDHaultfoeuille / twowayfeweights: column(s) {missing!r} " + f"not found in data. Required columns: outcome, group, time, treatment." + ) + + df = data.copy() + + # 1b. Group and time NaN checks (before groupby, which silently drops NaN keys) + n_nan_group = int(df[group].isna().sum()) + if n_nan_group > 0: + raise ValueError( + f"Group column {group!r} contains {n_nan_group} NaN value(s). " + "groupby silently drops NaN keys, which would change the " + "estimation sample without warning. Drop or impute NaN group " + "values before calling fit() or twowayfeweights()." + ) + n_nan_time = int(df[time].isna().sum()) + if n_nan_time > 0: + raise ValueError( + f"Time column {time!r} contains {n_nan_time} NaN value(s). " + "groupby silently drops NaN keys, which would change the " + "estimation sample without warning. Drop or impute NaN time " + "values before calling fit() or twowayfeweights()." + ) + + # 2. Treatment numeric coercion + NaN check + try: + df[treatment] = pd.to_numeric(df[treatment]) + except (ValueError, TypeError) as exc: + raise ValueError( + f"Could not coerce treatment column {treatment!r} to numeric: {exc}" + ) from exc + n_nan_treat = int(df[treatment].isna().sum()) + if n_nan_treat > 0: + raise ValueError( + f"Treatment column {treatment!r} contains {n_nan_treat} NaN value(s). " + "ChaisemartinDHaultfoeuille requires non-missing treatment indicators " + "on every observation; impute or drop NaN treatment rows before fitting " + "so the dropped count is explicit." + ) + + # 3. Outcome numeric coercion + NaN check + try: + df[outcome] = pd.to_numeric(df[outcome]) + except (ValueError, TypeError) as exc: + raise ValueError(f"Could not coerce outcome column {outcome!r} to numeric: {exc}") from exc + n_nan_outcome = int(df[outcome].isna().sum()) + if n_nan_outcome > 0: + raise ValueError( + f"Outcome column {outcome!r} contains {n_nan_outcome} NaN value(s). " + "Drop or impute missing outcomes before calling fit() so the " + "exclusion is explicit (silently averaging over present values " + "would distort per-cell means)." + ) + + # 4. Binary treatment validation (raw values, before aggregation) + unique_treats = pd.unique(df[treatment]) + invalid = [v for v in unique_treats if v not in (0, 1, 0.0, 1.0)] + if invalid: + raise ValueError( + f"ChaisemartinDHaultfoeuille / twowayfeweights requires binary treatment " + f"in {{0, 1}}; found values {invalid[:5]} in column {treatment!r}. " + "Non-binary treatment is reserved for Phase 3 of the dCDH rollout " + "(see ROADMAP.md Phase 3)." + ) + + # 5. Cell aggregation + cell = df.groupby([group, time], as_index=False).agg( + y_gt=(outcome, "mean"), + d_gt=(treatment, "mean"), + n_gt=(treatment, "count"), + ) + + # 6. Within-cell-varying treatment rejection + non_constant_mask = (cell["d_gt"] > 0) & (cell["d_gt"] < 1) + if non_constant_mask.any(): + n_non_constant = int(non_constant_mask.sum()) + example_cells = cell.loc[non_constant_mask, [group, time, "d_gt"]].head(5) + raise ValueError( + f"Within-cell-varying treatment detected in {n_non_constant} " + f"(group, time) cell(s). Phase 1 dCDH requires treatment to be " + f"constant within each (group, time) cell; fractional d_gt values " + f"indicate that some units in a cell are treated while others are " + f"not. Pre-aggregate your data to constant binary cell-level " + f"treatment before calling fit() or twowayfeweights(). Fuzzy DiD " + f"is deferred to a separate dCDH paper (see ROADMAP.md " + f"out-of-scope). Affected cells (first 5):\n{example_cells}" + ) + cell["d_gt"] = cell["d_gt"].astype(int) + + # Sort to ensure deterministic order in downstream operations + cell = cell.sort_values([group, time]).reset_index(drop=True) + return cell + + +# ============================================================================= +# Main estimator class +# ============================================================================= + + +class ChaisemartinDHaultfoeuille(ChaisemartinDHaultfoeuilleBootstrapMixin): + """ + de Chaisemartin-D'Haultfoeuille (dCDH) estimator — Phase 1. + + Computes the contemporaneous-switch DiD ``DID_M`` from the AER 2020 + paper, equivalently ``DID_1`` (horizon ``l = 1``) of the dynamic + companion paper (NBER WP 29873). The estimator is the only modern + DiD in the library that handles **reversible (non-absorbing) + treatments** — treatment may switch on AND off over time. + + Phase 1 deliverables: + + - The headline ``DID_M`` point estimate + - Joiners-only ``DID_+`` and leavers-only ``DID_-`` decompositions + - The single-lag placebo ``DID_M^pl`` (computed automatically by + default; gate via ``placebo=False``) + - Analytical SE via the cohort-recentered plug-in formula from + Web Appendix Section 3.7.3 of the dynamic paper + - Optional multiplier bootstrap clustered at the group level + - Optional TWFE decomposition diagnostic from Theorem 1 of AER 2020 + (per-cell weights, fraction negative, ``sigma_fe``) + + Parameters + ---------- + alpha : float, default=0.05 + Significance level for confidence intervals. + cluster : str, optional, default=None + **Phase 1 contract:** ``cluster`` must be ``None`` (the default). + dCDH always clusters at the group level via the cohort-recentered + influence-function plug-in (analytical SEs) and the multiplier + bootstrap (also grouped at the ``group`` column). Passing any + non-``None`` value raises ``NotImplementedError`` with a Phase 1 + pointer. Custom clustering at a coarser or finer level than the + group is reserved for a future phase. See REGISTRY.md + ``ChaisemartinDHaultfoeuille`` section for the full contract. + n_bootstrap : int, default=0 + Number of multiplier-bootstrap iterations. ``0`` (default) uses + only the analytical SE. Set to ``999`` or higher for stable + bootstrap inference. + bootstrap_weights : str, default="rademacher" + Type of multiplier-bootstrap weights: ``"rademacher"``, + ``"mammen"``, or ``"webb"``. Ignored unless ``n_bootstrap > 0``. + seed : int, optional + Random seed for the multiplier bootstrap. + placebo : bool, default=True + If ``True`` (default), automatically compute the single-lag + placebo ``DID_M^pl`` (AER 2020 placebo specification) on the same data. + Set to ``False`` to skip the placebo computation for speed; the + results object will still expose ``placebo_*`` fields, but with + NaN values and ``placebo_available=False``. + twfe_diagnostic : bool, default=True + If ``True`` (default), compute the TWFE decomposition diagnostic + from Theorem 1 of AER 2020: per-``(g, t)`` weights, fraction of + treated cells with negative weights, and ``sigma_fe`` (the + smallest cell-effect standard deviation that could flip the sign + of the plain TWFE coefficient). The diagnostic answers "what + would the plain TWFE estimator say on the data you passed in?", + so it runs on the **FULL pre-filter cell sample** (the same + input as the standalone :func:`twowayfeweights` function), NOT + on the post-filter estimation sample used by ``DID_M``. When + the ragged-panel filter or ``drop_larger_lower`` drops groups, + the fitted ``results.twfe_*`` values describe a LARGER sample + (pre-filter) than ``results.overall_att`` and a ``UserWarning`` + is emitted to make the divergence explicit. See REGISTRY.md + ``ChaisemartinDHaultfoeuille`` ``Note (TWFE diagnostic sample + contract)`` for the full rationale. + drop_larger_lower : bool, default=True + If ``True`` (default, matches R ``DIDmultiplegtDYN``), drops + groups whose treatment switches more than once (multi-switch + groups) before estimation. This is required for the analytical + variance formula to be consistent with the AER 2020 Theorem 3 + point estimate — both formulas operate on the same post-drop + dataset. Setting to ``False`` is supported for diagnostic + comparison but produces an inconsistent estimator-variance + pairing for multi-switch groups; a warning is emitted. + rank_deficient_action : str, default="warn" + Action when the TWFE decomposition diagnostic OLS encounters a + rank-deficient design matrix: ``"warn"``, ``"error"``, or + ``"silent"``. Only used when ``twfe_diagnostic=True``. + + Attributes + ---------- + results_ : ChaisemartinDHaultfoeuilleResults + Estimation results after calling :meth:`fit`. + is_fitted_ : bool + Whether the model has been fitted. + + Notes + ----- + The analytical CI is **conservative** under Assumption 8 (independent + groups) of the dynamic companion paper, and exact only under iid + sampling. This is documented as a deliberate deviation from "default + nominal coverage" in ``REGISTRY.md``. + + Examples + -------- + Basic single-switch panel: + + >>> from diff_diff import ChaisemartinDHaultfoeuille + >>> from diff_diff.prep_dgp import generate_reversible_did_data + >>> data = generate_reversible_did_data(n_groups=80, n_periods=6, seed=42) + >>> est = ChaisemartinDHaultfoeuille() + >>> results = est.fit( + ... data, outcome="outcome", group="group", + ... time="period", treatment="treatment", + ... ) + >>> abs(results.overall_att - 2.0) < 1.0 # close to the true effect + True + """ + + def __init__( + self, + alpha: float = 0.05, + cluster: Optional[str] = None, + n_bootstrap: int = 0, + bootstrap_weights: str = "rademacher", + seed: Optional[int] = None, + placebo: bool = True, + twfe_diagnostic: bool = True, + drop_larger_lower: bool = True, + rank_deficient_action: str = "warn", + ) -> None: + # Parameter validation + if rank_deficient_action not in ("warn", "error", "silent"): + raise ValueError( + f"rank_deficient_action must be 'warn', 'error', or 'silent', " + f"got '{rank_deficient_action}'" + ) + if bootstrap_weights not in ("rademacher", "mammen", "webb"): + raise ValueError( + f"bootstrap_weights must be 'rademacher', 'mammen', or 'webb', " + f"got '{bootstrap_weights}'" + ) + if not 0.0 < alpha < 1.0: + raise ValueError(f"alpha must be in (0, 1), got {alpha}") + if n_bootstrap < 0: + raise ValueError(f"n_bootstrap must be non-negative, got {n_bootstrap}") + if cluster is not None: + raise NotImplementedError( + f"cluster={cluster!r}: custom clustering is not supported in " + f"Phase 1 of ChaisemartinDHaultfoeuille. dCDH always clusters " + f"at the group level via the cohort-recentered influence-" + f"function plug-in (analytical SEs) and the multiplier " + f"bootstrap (also grouped at the group column). To use the " + f"supported group-level clustering, pass cluster=None (the " + f"default). Custom clustering is reserved for a future " + f"phase. See REGISTRY.md ChaisemartinDHaultfoeuille section " + f"for the full contract." + ) + + self.alpha = alpha + self.cluster = cluster + self.n_bootstrap = n_bootstrap + self.bootstrap_weights = bootstrap_weights + self.seed = seed + self.placebo = placebo + self.twfe_diagnostic = twfe_diagnostic + self.drop_larger_lower = drop_larger_lower + self.rank_deficient_action = rank_deficient_action + + self.is_fitted_ = False + self.results_: Optional[ChaisemartinDHaultfoeuilleResults] = None + + # ------------------------------------------------------------------ + # sklearn-style parameter introspection + # ------------------------------------------------------------------ + + def get_params(self) -> Dict[str, Any]: + """Return all ``__init__`` parameters as a dictionary.""" + return { + "alpha": self.alpha, + "cluster": self.cluster, + "n_bootstrap": self.n_bootstrap, + "bootstrap_weights": self.bootstrap_weights, + "seed": self.seed, + "placebo": self.placebo, + "twfe_diagnostic": self.twfe_diagnostic, + "drop_larger_lower": self.drop_larger_lower, + "rank_deficient_action": self.rank_deficient_action, + } + + def set_params(self, **params: Any) -> "ChaisemartinDHaultfoeuille": + """ + Set estimator parameters (sklearn-compatible). + + Re-runs the same validation rules as ``__init__`` so invalid + parameter combinations cannot be introduced after construction. + """ + for key, value in params.items(): + if not hasattr(self, key): + raise ValueError(f"Unknown parameter: {key}") + setattr(self, key, value) + + # Re-run __init__ validation rules so the post-set state is valid. + if self.rank_deficient_action not in ("warn", "error", "silent"): + raise ValueError( + f"rank_deficient_action must be 'warn', 'error', or 'silent', " + f"got '{self.rank_deficient_action}'" + ) + if self.bootstrap_weights not in ("rademacher", "mammen", "webb"): + raise ValueError( + f"bootstrap_weights must be 'rademacher', 'mammen', or 'webb', " + f"got '{self.bootstrap_weights}'" + ) + if not 0.0 < self.alpha < 1.0: + raise ValueError(f"alpha must be in (0, 1), got {self.alpha}") + if self.n_bootstrap < 0: + raise ValueError(f"n_bootstrap must be non-negative, got {self.n_bootstrap}") + if self.cluster is not None: + raise NotImplementedError( + f"cluster={self.cluster!r}: custom clustering is not supported " + f"in Phase 1 of ChaisemartinDHaultfoeuille. dCDH always clusters " + f"at the group level. To use the supported group-level " + f"clustering, pass cluster=None (the default). Custom clustering " + f"is reserved for a future phase. See REGISTRY.md " + f"ChaisemartinDHaultfoeuille section for the full contract." + ) + return self + + # ------------------------------------------------------------------ + # fit + # ------------------------------------------------------------------ + + def fit( + self, + data: pd.DataFrame, + outcome: str, + group: str, + time: str, + treatment: str, + # ---------- forward-compat parameters ---------- + aggregate: Optional[str] = None, + L_max: Optional[int] = None, + controls: Optional[List[str]] = None, + trends_linear: Optional[bool] = None, + trends_nonparam: Optional[Any] = None, + honest_did: bool = False, + # ---------- deferred (separate effort) ---------- + survey_design: Any = None, + ) -> ChaisemartinDHaultfoeuilleResults: + """ + Fit the dCDH estimator on individual-level panel data. + + Parameters + ---------- + data : pd.DataFrame + Individual-level panel. Must contain columns for ``outcome``, + ``group``, ``time``, and ``treatment``. The estimator + internally aggregates to ``(group, time)`` cells. + outcome : str + Outcome variable column name. + group : str + Group identifier column name. Treatment must be constant + within each ``(group, time)`` cell after aggregation; + ``ValueError`` is raised if any cell has fractional + treatment after grouping (within-cell-varying treatment + indicates a fuzzy design not supported in Phase 1). + time : str + Time period column name. Must be sortable. + treatment : str + Per-observation binary treatment column. Must coerce to + ``{0, 1}``; non-binary values raise ``ValueError`` (Phase 3 + adds non-binary support). + aggregate : str, optional + **Reserved for Phase 2.** Phase 1 requires ``aggregate=None``; + any other value raises ``NotImplementedError``. + L_max : int, optional + **Reserved for Phase 2** (multi-horizon event study). + controls : list of str, optional + **Reserved for Phase 3** (covariate adjustment via the + residualization-style ``DID^X`` from Web Appendix Section 1.2 + of the dynamic paper). + trends_linear : bool, optional + **Reserved for Phase 3** (group-specific linear trends via + ``DID^{fd}``). + trends_nonparam : Any, optional + **Reserved for Phase 3** (state-set-specific trends). + honest_did : bool, default=False + **Reserved for Phase 3** (HonestDiD integration on placebos). + survey_design : Any, optional + **Not supported in any phase.** Survey design integration is + handled as a separate effort after all three phases ship. + Passing a non-``None`` value raises ``NotImplementedError``. + + Returns + ------- + ChaisemartinDHaultfoeuilleResults + + Raises + ------ + ValueError + If required columns are missing, treatment is not binary, or + the panel has too few groups / periods. + NotImplementedError + If any forward-compat parameter is set to a non-default + value, with a clear pointer to the relevant ROADMAP phase. + """ + # ------------------------------------------------------------------ + # Step 1: Column validation + # ------------------------------------------------------------------ + required_cols = [outcome, group, time, treatment] + missing = [c for c in required_cols if c not in data.columns] + if missing: + raise ValueError(f"Missing columns: {missing}") + + # ------------------------------------------------------------------ + # Step 2: Forward-compat gates + # ------------------------------------------------------------------ + _check_forward_compat_gates( + aggregate=aggregate, + L_max=L_max, + controls=controls, + trends_linear=trends_linear, + trends_nonparam=trends_nonparam, + honest_did=honest_did, + ) + + # ------------------------------------------------------------------ + # Step 3: Survey gate (deferred separate effort) + # ------------------------------------------------------------------ + if survey_design is not None: + raise NotImplementedError( + "ChaisemartinDHaultfoeuille does not support survey_design. " + "Survey design integration for dCDH is deferred to a separate " + "effort after all three implementation phases ship (see " + "ROADMAP.md out-of-scope section). For now, fit without " + "survey_design. If your treatment is absorbing, use " + "CallawaySantAnna which supports survey_design." + ) + + # ------------------------------------------------------------------ + # Step 4-5: Validate input + aggregate to (g, t) cells via the + # shared helper used by both fit() and twowayfeweights(). The + # helper enforces NaN/binary/within-cell-rounding rules from + # REGISTRY.md and returns a sorted cell DataFrame with columns + # [group, time, y_gt, d_gt, n_gt]. + # ------------------------------------------------------------------ + cell = _validate_and_aggregate_to_cells( + data=data, + outcome=outcome, + group=group, + time=time, + treatment=treatment, + ) + + # ------------------------------------------------------------------ + # Step 5a: Compute the TWFE diagnostic on the FULL pre-filter cell + # dataset, so the diagnostic reflects the data the user + # actually passed in. This MUST run BEFORE Step 5b (the + # ragged-panel filter) so that the fitted diagnostic and + # the standalone twowayfeweights() function produce + # identical results on ragged panels — both operate on + # the same _validate_and_aggregate_to_cells() output. + # ------------------------------------------------------------------ + twfe_diagnostic_payload = None + if self.twfe_diagnostic: + try: + twfe_diagnostic_payload = _compute_twfe_diagnostic( + cell=cell, + group_col=group, + time_col=time, + rank_deficient_action=self.rank_deficient_action, + ) + except Exception as exc: # noqa: BLE001 + # Honor rank_deficient_action="error": if the user + # explicitly requested strict failure on rank-deficient + # designs, re-raise instead of downgrading to a warning. + # Only genuinely non-fatal failures (e.g., numerical + # issues unrelated to rank deficiency) should be + # swallowed as warnings. + if self.rank_deficient_action == "error" and isinstance(exc, ValueError): + raise + warnings.warn( + f"TWFE decomposition diagnostic failed: {exc}. " + "Skipping diagnostic; main estimation continues.", + UserWarning, + stacklevel=2, + ) + twfe_diagnostic_payload = None + + # ------------------------------------------------------------------ + # Step 5b: Ragged panel validation + # + # The cohort/variance path treats D_{g,1} as the canonical + # baseline and walks adjacent observed periods to detect first + # switches. Ragged panels with missing baseline rows or interior + # gaps would either crash the cohort enumeration (NaN -> int + # cast) or silently misclassify cohorts. Two-tier handling: + # + # (a) Reject groups missing the FIRST GLOBAL period (the + # baseline) with a clear ValueError listing offenders. + # (b) Drop groups with INTERIOR GAPS (missing intermediate + # periods between their first and last observed period) + # with an explicit UserWarning. + # ------------------------------------------------------------------ + all_periods_pre_drop = sorted(cell[time].unique().tolist()) + if len(all_periods_pre_drop) < 2: + raise ValueError( + f"ChaisemartinDHaultfoeuille requires at least 2 distinct time " + f"periods in the panel, got {len(all_periods_pre_drop)}." + ) + first_global_period = all_periods_pre_drop[0] + + # (a) Reject groups missing the first global period + groups_with_baseline = set(cell.loc[cell[time] == first_global_period, group].tolist()) + all_groups_pre_validation = set(cell[group].unique().tolist()) + groups_missing_baseline = sorted(all_groups_pre_validation - groups_with_baseline) + if groups_missing_baseline: + raise ValueError( + f"ChaisemartinDHaultfoeuille requires every group to have an " + f"observation at the first global period " + f"(period={first_global_period!r}). " + f"{len(groups_missing_baseline)} group(s) are missing this baseline. " + f"Examples: {groups_missing_baseline[:5]}" + + ( + f" (and {len(groups_missing_baseline) - 5} more)" + if len(groups_missing_baseline) > 5 + else "" + ) + + ". Drop these groups or back-fill the baseline before fitting " + "so the exclusion is explicit." + ) + + # (b) Drop groups with interior gaps + period_index = {p: i for i, p in enumerate(all_periods_pre_drop)} + groups_with_interior_gaps: List[Any] = [] + for g_id, sub in cell.groupby(group): + g_periods = sub[time].tolist() + g_min_idx = period_index[min(g_periods)] + g_max_idx = period_index[max(g_periods)] + expected_count = g_max_idx - g_min_idx + 1 + if len(g_periods) != expected_count: + groups_with_interior_gaps.append(g_id) + n_groups_dropped_interior_gap = len(groups_with_interior_gaps) + if groups_with_interior_gaps: + warnings.warn( + f"Dropping {len(groups_with_interior_gaps)} group(s) with interior " + f"period gaps (missing observations between their first and last " + f"observed period). Examples: {groups_with_interior_gaps[:5]}" + + ( + f" (and {len(groups_with_interior_gaps) - 5} more)" + if len(groups_with_interior_gaps) > 5 + else "" + ) + + ". dCDH requires consecutive observed periods for the " + "cohort/variance path; back-fill or interpolate the missing " + "periods if you want these groups in the estimation.", + UserWarning, + stacklevel=2, + ) + cell = cell[~cell[group].isin(groups_with_interior_gaps)].reset_index(drop=True) + if cell.empty: + raise ValueError( + "After dropping groups with interior period gaps, no groups " + "remain. Provide a balanced panel or back-fill missing periods." + ) + + all_periods_pre_drop = sorted(cell[time].unique().tolist()) + if len(all_periods_pre_drop) < 2: + raise ValueError( + f"ChaisemartinDHaultfoeuille requires at least 2 periods, " + f"got {len(all_periods_pre_drop)}" + ) + + # ------------------------------------------------------------------ + # Step 6: Drop A5-violating (multi-switch) cells per drop_larger_lower + # ------------------------------------------------------------------ + n_groups_dropped_crossers = 0 + if self.drop_larger_lower: + cell, n_groups_dropped_crossers = _drop_crossing_cells( + cell=cell, group_col=group, d_col="d_gt" + ) + else: + warnings.warn( + "drop_larger_lower=False: the analytical variance formula will " + "be inconsistent with the point estimate for any multi-switch " + "groups present in the data, producing a biased SE. Use only " + "for diagnostic comparison against R or when you are confident " + "no multi-switch groups exist.", + UserWarning, + stacklevel=2, + ) + + # ------------------------------------------------------------------ + # Step 6b: TWFE diagnostic sample-contract notice + # + # The fitted twfe_* values (if the diagnostic succeeded in + # Step 5a) were computed on the FULL pre-filter cell sample, + # matching the standalone twowayfeweights() output. Steps 5b + # and 6 may have dropped groups since then. When they did, the + # fitted diagnostic and the dCDH point estimate describe + # DIFFERENT samples, so we surface that divergence as a + # UserWarning per the REGISTRY contract Note. Users see the + # warning at fit time and can decide whether to pre-process + # their data before re-fitting (or accept the documented + # divergence). + # + # The warning fires whenever the user requested the diagnostic + # AND filters dropped groups, even if _compute_twfe_diagnostic + # itself failed (rank-deficient fallback) and + # twfe_diagnostic_payload is None. The warning text uses "(if + # the diagnostic succeeded)" to remain accurate in both cases. + # ------------------------------------------------------------------ + if self.twfe_diagnostic and (n_groups_dropped_interior_gap + n_groups_dropped_crossers) > 0: + warnings.warn( + f"TWFE diagnostic sample-contract notice: the dCDH point " + f"estimate, results.groups, and inference fields use a " + f"POST-FILTER sample after Step 5b dropped " + f"{n_groups_dropped_interior_gap} interior-gap group(s) " + f"and Step 6 dropped {n_groups_dropped_crossers} multi-" + f"switch group(s). The fitted results.twfe_* values (if " + f"the diagnostic succeeded) were computed on the FULL " + f"pre-filter cell sample, so they describe a LARGER " + f"sample (pre-filter) than overall_att. The standalone " + f"twowayfeweights() function also uses the pre-filter " + f"sample. This is the documented Phase 1 contract — see " + f"REGISTRY.md ChaisemartinDHaultfoeuille `Note (TWFE " + f"diagnostic sample contract)` for the rationale. To " + f"reproduce the dCDH estimation sample for an external " + f"TWFE comparison, pre-process your data to drop the " + f"{n_groups_dropped_interior_gap + n_groups_dropped_crossers} " + f"flagged groups before re-fitting.", + UserWarning, + stacklevel=2, + ) + + # ------------------------------------------------------------------ + # Step 7: Singleton-baseline identification (footnote 15 of dynamic paper) + # ------------------------------------------------------------------ + # The singleton-baseline filter identifies groups whose baseline + # treatment value D_{g,1} is unique in the panel. Per footnote 15 + # of the dynamic paper, these have no baseline-matched cohort peer + # and contribute zero variance under the cohort framework. + # + # IMPORTANT: under Python's documented period-based stable-control + # interpretation, a singleton-baseline group can STILL be a valid + # stable_0 / stable_1 control for the point estimate, even though + # it has no cohort peer. The filter is therefore applied at the + # variance stage only — the cell DataFrame retains these groups + # so they can serve as stable controls. + # Use the validated first global period as the canonical baseline. + # Step 5b guarantees every group has an observation at this period, + # so we can read it directly without a groupby.first() that could + # otherwise return a later observed period for late-entry groups. + baselines_per_group = cell.loc[cell[time] == first_global_period, [group, "d_gt"]].rename( + columns={"d_gt": "_baseline"} + ) + baseline_counts = baselines_per_group["_baseline"].value_counts() + singleton_baseline_values = baseline_counts[baseline_counts < 2].index.tolist() + singleton_baseline_groups: List[Any] = ( + baselines_per_group.loc[ + baselines_per_group["_baseline"].isin(singleton_baseline_values), group + ].tolist() + if singleton_baseline_values + else [] + ) + n_groups_dropped_singleton_baseline = len(singleton_baseline_groups) + if n_groups_dropped_singleton_baseline > 0: + warnings.warn( + f"Singleton-baseline filter (footnote 15 of dynamic paper): " + f"{n_groups_dropped_singleton_baseline} group(s) excluded from " + f"the cohort-recentered VARIANCE computation only — they remain " + f"in the point-estimate sample as period-based stable controls. " + f"Examples: {singleton_baseline_groups[:5]}" + + ( + f" (and {n_groups_dropped_singleton_baseline - 5} more)" + if n_groups_dropped_singleton_baseline > 5 + else "" + ), + UserWarning, + stacklevel=2, + ) + + if cell.empty or cell[group].nunique() == 0: + raise ValueError( + "After dropping multi-switch cells (drop_larger_lower=True), no " + "groups remain. The dataset cannot support dCDH estimation. " + "Check the input panel for diversity in treatment patterns." + ) + + # Determine the post-filter group set, period set, and per-group state + all_groups = sorted(cell[group].unique().tolist()) + all_periods = sorted(cell[time].unique().tolist()) + n_obs_post = int(cell["n_gt"].sum()) + + # Pivot to (group x time) matrices for vectorized computations + d_pivot = cell.pivot(index=group, columns=time, values="d_gt").reindex( + index=all_groups, columns=all_periods + ) + y_pivot = cell.pivot(index=group, columns=time, values="y_gt").reindex( + index=all_groups, columns=all_periods + ) + n_pivot = ( + cell.pivot(index=group, columns=time, values="n_gt") + .reindex(index=all_groups, columns=all_periods) + .fillna(0) + .astype(int) + ) + D_mat = d_pivot.to_numpy() + Y_mat = y_pivot.to_numpy() + N_mat = n_pivot.to_numpy() + + # ------------------------------------------------------------------ + # Step 8-9: Switching-cell counts and per-period DIDs (Theorem 3) + # with explicit A11 zero-retention pseudocode + # ------------------------------------------------------------------ + ( + per_period_effects, + a11_warnings, + did_plus_t_arr, + did_minus_t_arr, + n_10_t_arr, + n_01_t_arr, + n_00_t_arr, + n_11_t_arr, + a11_plus_zeroed_arr, + a11_minus_zeroed_arr, + ) = _compute_per_period_dids( + D_mat=D_mat, + Y_mat=Y_mat, + N_mat=N_mat, + periods=all_periods, + ) + if a11_warnings: + warnings.warn( + f"Assumption 11 (existence of stable controls) violated in " + f"{len(a11_warnings)} period(s); the affected DID_+/DID_- values " + f"are zeroed but their switcher counts are retained in the N_S " + f"denominator (matching paper convention). Affected: " + f"{', '.join(a11_warnings[:3])}" + + (f" (and {len(a11_warnings) - 3} more)" if len(a11_warnings) > 3 else ""), + UserWarning, + stacklevel=2, + ) + + # ------------------------------------------------------------------ + # Step 10: Aggregate DID_M = sum_t (n_10_t * did_plus_t + n_01_t * did_minus_t) / N_S + # ------------------------------------------------------------------ + N_S = int(n_10_t_arr.sum() + n_01_t_arr.sum()) + if N_S == 0: + raise ValueError( + "No switching cells found in the data after filtering: every " + "group has constant treatment for the entire panel. dCDH " + "requires at least one (g, t) cell where the group's treatment " + "differs from the previous period." + ) + overall_att = float((n_10_t_arr @ did_plus_t_arr + n_01_t_arr @ did_minus_t_arr) / N_S) + + # ------------------------------------------------------------------ + # Step 11: Joiners and leavers views + # ------------------------------------------------------------------ + joiner_total = int(n_10_t_arr.sum()) + leaver_total = int(n_01_t_arr.sum()) + joiners_available = joiner_total > 0 + leavers_available = leaver_total > 0 + if joiners_available: + joiners_att = float((n_10_t_arr @ did_plus_t_arr) / joiner_total) + else: + joiners_att = float("nan") + if leavers_available: + leavers_att = float((n_01_t_arr @ did_minus_t_arr) / leaver_total) + else: + leavers_att = float("nan") + + # Joiner / leaver sample-size metadata. + # n_*_cells: total switching cells across all periods (sum of per-period + # cell counts; each (g, t) joiner/leaver cell counted once). + # n_*_obs: actual observation count (sum of n_gt over the same cells), + # which differs from cells when individual-level inputs have + # multiple original observations per (g, t). + n_joiner_cells = int(n_10_t_arr.sum()) + n_leaver_cells = int(n_01_t_arr.sum()) + n_joiner_obs = 0 + n_leaver_obs = 0 + for t_idx in range(1, len(all_periods)): + d_curr = D_mat[:, t_idx] + d_prev = D_mat[:, t_idx - 1] + n_curr = N_mat[:, t_idx] + n_prev = N_mat[:, t_idx - 1] + present = (n_curr > 0) & (n_prev > 0) + joiner_mask_t = (d_prev == 0) & (d_curr == 1) & present + leaver_mask_t = (d_prev == 1) & (d_curr == 0) & present + n_joiner_obs += int(n_curr[joiner_mask_t].sum()) + n_leaver_obs += int(n_curr[leaver_mask_t].sum()) + + # ------------------------------------------------------------------ + # Step 12: Placebo (DID_M^pl) + # ------------------------------------------------------------------ + placebo_available = False + placebo_effect = float("nan") + if self.placebo: + if len(all_periods) < 3: + warnings.warn( + f"Placebo DID_M^pl requires at least 3 time " + f"periods; the post-filter panel has only {len(all_periods)}. " + "Skipping the placebo computation. Pass placebo=False to " + "suppress this warning, or use a panel with T >= 3.", + UserWarning, + stacklevel=2, + ) + else: + placebo_payload = _compute_placebo( + D_mat=D_mat, Y_mat=Y_mat, N_mat=N_mat, periods=all_periods + ) + if placebo_payload is None: + warnings.warn( + "Placebo DID_M^pl could not be computed: no qualifying " + "switching cells with the required 3-period stable " + "history exist after filtering. The placebo fields on " + "the results object are NaN with placebo_available=False.", + UserWarning, + stacklevel=2, + ) + else: + placebo_effect, placebo_available, placebo_a11_warnings = placebo_payload + # Surface placebo A11 violations via a consolidated warning + # mirroring the main DID path's contract. The affected + # per-period placebo contributions are zeroed in the + # numerator with their switcher counts retained in the + # placebo N_S^pl denominator (placebo zero-retention). + if placebo_a11_warnings: + warnings.warn( + f"Placebo (DID_M^pl) Assumption 11 violations in " + f"{len(placebo_a11_warnings)} period(s); the affected " + f"placebo contributions are zeroed but their switcher " + f"counts are retained in the placebo N_S denominator " + f"(matching placebo paper convention). Affected: " + + ", ".join(placebo_a11_warnings[:3]) + + ( + f" (and {len(placebo_a11_warnings) - 3} more)" + if len(placebo_a11_warnings) > 3 + else "" + ), + UserWarning, + stacklevel=2, + ) + + # ------------------------------------------------------------------ + # Step 13-16: Cohort identification, influence-function vectors, + # cohort-recentered plug-in variance + # ------------------------------------------------------------------ + ( + U_centered_overall, + n_groups_for_overall_var, + n_cohorts, + n_groups_dropped_never_switching, + U_centered_joiners, + U_centered_leavers, + ) = _compute_cohort_recentered_inputs( + D_mat=D_mat, + Y_mat=Y_mat, + N_mat=N_mat, + n_10_t_arr=n_10_t_arr, + n_00_t_arr=n_00_t_arr, + n_01_t_arr=n_01_t_arr, + n_11_t_arr=n_11_t_arr, + a11_plus_zeroed_arr=a11_plus_zeroed_arr, + a11_minus_zeroed_arr=a11_minus_zeroed_arr, + all_groups=all_groups, + singleton_baseline_groups=singleton_baseline_groups, + ) + + # Analytical SE for DID_M + overall_se = _plugin_se(U_centered=U_centered_overall, divisor=N_S) + # Detect the degenerate-cohort case: every variance-eligible group + # forms its own (D_{g,1}, F_g, S_g) cohort, so the centered + # influence function is identically zero and `_plugin_se` returns + # NaN. Surface this as a UserWarning so users see the variance is + # unidentified rather than silently mistaking NaN for "missing + # data" or 0.0 for infinite precision. The bootstrap path inherits + # the same degeneracy on this panel because it multiplies the + # same all-zero centered IF by random weights. + if np.isnan(overall_se) and n_groups_for_overall_var > 0 and N_S > 0: + warnings.warn( + f"Cohort-recentered analytical variance is unidentified: " + f"every variance-eligible group forms its own " + f"(D_{{g,1}}, F_g, S_g) cohort " + f"({n_groups_for_overall_var} groups across {n_cohorts} " + f"cohorts), so the centered influence function vector is " + f"identically zero. The DID_M point estimate is still " + f"valid; SE / t_stat / p_value / conf_int are NaN-" + f"consistent. To get a non-degenerate analytical SE, " + f"include more groups so cohorts have peers (real-world " + f"panels typically have G >> K). The bootstrap path " + f"inherits the same degeneracy on this data.", + UserWarning, + stacklevel=2, + ) + overall_t, overall_p, overall_ci = safe_inference( + overall_att, overall_se, alpha=self.alpha, df=None + ) + + # Joiners SE (uses joiner-only centered IF; conservative bound) + if joiners_available: + joiners_se = _plugin_se(U_centered=U_centered_joiners, divisor=joiner_total) + joiners_t, joiners_p, joiners_ci = safe_inference( + joiners_att, joiners_se, alpha=self.alpha, df=None + ) + else: + joiners_se, joiners_t, joiners_p, joiners_ci = ( + float("nan"), + float("nan"), + float("nan"), + (float("nan"), float("nan")), + ) + + # Leavers SE + if leavers_available: + leavers_se = _plugin_se(U_centered=U_centered_leavers, divisor=leaver_total) + leavers_t, leavers_p, leavers_ci = safe_inference( + leavers_att, leavers_se, alpha=self.alpha, df=None + ) + else: + leavers_se, leavers_t, leavers_p, leavers_ci = ( + float("nan"), + float("nan"), + float("nan"), + (float("nan"), float("nan")), + ) + + # Placebo SE: intentionally NaN in Phase 1. The dynamic paper + # derives the cohort-recentered analytical variance for DID_l only, + # not for the placebo. Phase 2 will add multiplier-bootstrap + # support for the placebo. See REGISTRY.md placebo SE Note. + placebo_se = float("nan") + placebo_t = float("nan") + placebo_p = float("nan") + placebo_ci: Tuple[float, float] = (float("nan"), float("nan")) + if placebo_available: + # Phase 1: the dynamic companion paper Section 3.7.3 derives the + # cohort-recentered analytical variance for DID_l only — not for + # the placebo DID_M^pl. The placebo bootstrap path is also + # deferred to Phase 2 (the bootstrap mixin currently covers + # DID_M, DID_+, and DID_- only). The placebo point estimate is + # still computed and exposed via results.placebo_effect; only + # its inference fields stay NaN-consistent. + warnings.warn( + "Phase 1 placebo SE is intentionally NaN. The dynamic " + "companion paper Section 3.7.3 derives the cohort-recentered " + "analytical variance for DID_l only, not for the placebo " + "DID_M^pl. Phase 2 will add multiplier-bootstrap support for " + "the placebo; until then, placebo_se / placebo_t_stat / " + "placebo_p_value / placebo_conf_int stay NaN even when " + "n_bootstrap > 0. The placebo point estimate " + "(results.placebo_effect) is still meaningful.", + UserWarning, + stacklevel=2, + ) + + # ------------------------------------------------------------------ + # Step 18: Build per-period decomposition with explicit n_*_t fields + # ------------------------------------------------------------------ + n_treated_obs_post = int(N_mat[D_mat == 1].sum()) + + # ------------------------------------------------------------------ + # Step 19: Bootstrap if requested + # ------------------------------------------------------------------ + bootstrap_results: Optional[DCDHBootstrapResults] = None + if self.n_bootstrap > 0: + joiners_inputs = ( + (U_centered_joiners, joiner_total, joiners_att) if joiners_available else None + ) + leavers_inputs = ( + (U_centered_leavers, leaver_total, leavers_att) if leavers_available else None + ) + # Phase 1 placebo bootstrap: deliberately deferred to Phase 2. + # The dynamic companion paper Section 3.7.3 derives the + # cohort-recentered analytical variance for DID_l only, not for + # the placebo DID_M^pl, and we do not have an influence-function + # representation of the placebo to feed the multiplier bootstrap + # path. Implementing this from first principles is explicitly out + # of scope for Phase 1 — see ROADMAP.md and CHANGELOG.md. + # Tests/test_chaisemartin_dhaultfoeuille.py::TestBootstrap:: + # test_placebo_bootstrap_unavailable_in_phase_1 pins this contract. + placebo_inputs = None + + br = self._compute_dcdh_bootstrap( + n_groups_for_overall=n_groups_for_overall_var, + u_centered_overall=U_centered_overall, + divisor_overall=N_S, + original_overall=overall_att, + joiners_inputs=joiners_inputs, + leavers_inputs=leavers_inputs, + placebo_inputs=placebo_inputs, + ) + bootstrap_results = br + + # Replace the analytical SE with the bootstrap SE for the + # targets that have valid bootstrap output, AND propagate + # the bootstrap percentile p-value and CI directly to the + # top-level fields. The t-stat is computed from the SE via + # safe_inference()[0] so the project anti-pattern rule + # (never compute t_stat = effect / se inline) stays + # satisfied — bootstrap does not define an alternative + # t-stat semantic for percentile bootstrap, so the + # SE-based t-stat is the natural choice. + # + # Library precedent: imputation.py:790-805, + # two_stage.py:778-787, and efficient_did.py:1009-1013 all + # propagate bootstrap p/CI to the public surface while + # keeping a SE-derived t-stat. Round 10 brings dCDH in line + # with that pattern (the prior code silently recomputed + # normal-theory p/CI from the bootstrap SE, which made the + # public inference surface a hybrid). + # + # See REGISTRY.md ChaisemartinDHaultfoeuille `Note + # (bootstrap inference surface)` and the regression test + # ``test_bootstrap_p_value_and_ci_propagated_to_top_level``. + if np.isfinite(br.overall_se): + overall_se = br.overall_se + overall_p = br.overall_p_value if br.overall_p_value is not None else np.nan + overall_ci = br.overall_ci if br.overall_ci is not None else (np.nan, np.nan) + overall_t = safe_inference(overall_att, overall_se, alpha=self.alpha, df=None)[0] + if joiners_available and br.joiners_se is not None and np.isfinite(br.joiners_se): + joiners_se = br.joiners_se + joiners_p = br.joiners_p_value if br.joiners_p_value is not None else np.nan + joiners_ci = br.joiners_ci if br.joiners_ci is not None else (np.nan, np.nan) + joiners_t = safe_inference(joiners_att, joiners_se, alpha=self.alpha, df=None)[0] + if leavers_available and br.leavers_se is not None and np.isfinite(br.leavers_se): + leavers_se = br.leavers_se + leavers_p = br.leavers_p_value if br.leavers_p_value is not None else np.nan + leavers_ci = br.leavers_ci if br.leavers_ci is not None else (np.nan, np.nan) + leavers_t = safe_inference(leavers_att, leavers_se, alpha=self.alpha, df=None)[0] + + # ------------------------------------------------------------------ + # Step 20: Build the results dataclass + # ------------------------------------------------------------------ + # event_study_effects holds a single l=1 entry mirroring overall_att + # (per review MEDIUM #5: stable shape across phases). + event_study_effects = { + 1: { + "effect": overall_att, + "se": overall_se, + "t_stat": overall_t, + "p_value": overall_p, + "conf_int": overall_ci, + "n_obs": N_S, + } + } + + twfe_weights_df = None + twfe_fraction_negative = None + twfe_sigma_fe = None + twfe_beta_fe = None + if twfe_diagnostic_payload is not None: + twfe_weights_df = twfe_diagnostic_payload.weights + twfe_fraction_negative = twfe_diagnostic_payload.fraction_negative + twfe_sigma_fe = twfe_diagnostic_payload.sigma_fe + twfe_beta_fe = twfe_diagnostic_payload.beta_fe + + results = ChaisemartinDHaultfoeuilleResults( + overall_att=overall_att, + overall_se=overall_se, + overall_t_stat=overall_t, + overall_p_value=overall_p, + overall_conf_int=overall_ci, + joiners_att=joiners_att, + joiners_se=joiners_se, + joiners_t_stat=joiners_t, + joiners_p_value=joiners_p, + joiners_conf_int=joiners_ci, + n_joiner_cells=n_joiner_cells, + n_joiner_obs=n_joiner_obs, + joiners_available=joiners_available, + leavers_att=leavers_att, + leavers_se=leavers_se, + leavers_t_stat=leavers_t, + leavers_p_value=leavers_p, + leavers_conf_int=leavers_ci, + n_leaver_cells=n_leaver_cells, + n_leaver_obs=n_leaver_obs, + leavers_available=leavers_available, + placebo_effect=placebo_effect, + placebo_se=placebo_se, + placebo_t_stat=placebo_t, + placebo_p_value=placebo_p, + placebo_conf_int=placebo_ci, + placebo_available=placebo_available, + per_period_effects=per_period_effects, + groups=all_groups, + time_periods=all_periods, + n_obs=n_obs_post, + n_treated_obs=n_treated_obs_post, + n_switcher_cells=N_S, + n_cohorts=n_cohorts, + n_groups_dropped_crossers=n_groups_dropped_crossers, + n_groups_dropped_singleton_baseline=n_groups_dropped_singleton_baseline, + n_groups_dropped_never_switching=n_groups_dropped_never_switching, + event_study_effects=event_study_effects, + twfe_weights=twfe_weights_df, + twfe_fraction_negative=twfe_fraction_negative, + twfe_sigma_fe=twfe_sigma_fe, + twfe_beta_fe=twfe_beta_fe, + alpha=self.alpha, + bootstrap_results=bootstrap_results, + _estimator_ref=self, + ) + + self.results_ = results + self.is_fitted_ = True + return results + + +# ============================================================================= +# Module-level helpers +# ============================================================================= + + +def _check_forward_compat_gates( + aggregate: Optional[str], + L_max: Optional[int], + controls: Optional[List[str]], + trends_linear: Optional[bool], + trends_nonparam: Any, + honest_did: bool, +) -> None: + """Raise ``NotImplementedError`` for any non-default Phase 2/3 parameter.""" + if aggregate is not None: + # MEDIUM #1: strict equality with None — do not accept "simple" silently + raise NotImplementedError( + f"aggregate={aggregate!r} is reserved for Phase 2 of dCDH " + "(multi-horizon event study via DID_l). Phase 1 requires " + "aggregate=None and ships only DID_M = DID_1, the contemporaneous-" + "switch estimator at horizon l=1. See ROADMAP.md Phase 2." + ) + if L_max is not None: + raise NotImplementedError( + "L_max is reserved for Phase 2 of dCDH (multi-horizon event study). " + "Phase 1 computes only the l=1 effect DID_M. See ROADMAP.md Phase 2." + ) + if controls is not None: + raise NotImplementedError( + "Covariate adjustment (DID^X) is reserved for Phase 3 of dCDH, which " + "implements the residualization-style covariate adjustment from Web " + "Appendix Section 1.2 of the dynamic companion paper. Note: this is " + "NOT doubly-robust, NOT IPW, and NOT Callaway-Sant'Anna-style. " + "See ROADMAP.md Phase 3." + ) + if trends_linear is not None: + raise NotImplementedError( + "Group-specific linear trends (DID^{fd}) are reserved for Phase 3 of " + "dCDH (Web Appendix Section 1.3, Lemma 6 of the dynamic companion " + "paper). See ROADMAP.md Phase 3." + ) + if trends_nonparam is not None: + raise NotImplementedError( + "State-set-specific trends (trends_nonparam) are reserved for Phase 3 " + "of dCDH (Web Appendix Section 1.4). See ROADMAP.md Phase 3." + ) + if honest_did: + raise NotImplementedError( + "HonestDiD integration for dCDH is reserved for Phase 3, applied to " + "the placebo DID^{pl}_l output. Phase 1 provides only the placebo " + "point estimate via results.placebo_effect. See ROADMAP.md Phase 3." + ) + + +def _drop_crossing_cells( + cell: pd.DataFrame, group_col: str, d_col: str +) -> Tuple[pd.DataFrame, int]: + """ + Drop multi-switch groups (matches R DIDmultiplegtDYN drop_larger_lower=TRUE). + + For binary treatment in Phase 1, "multi-switch" means a group whose + treatment switches more than once across the panel. Such groups are + dropped entirely (not just the post-second-switch cells) so the + cohort identification step (which uses the first switch as the + cohort marker) and the variance computation operate on a consistent + dataset. + + Parameters + ---------- + cell : pd.DataFrame + Cell-level dataset with columns for ``group_col``, ``time_col``, + ``d_col``, and possibly other metadata. Must be sorted by group + and time. + group_col : str + d_col : str + Treatment column name. + + Returns + ------- + filtered : pd.DataFrame + Subset of ``cell`` with all multi-switch groups removed. + n_dropped : int + Number of groups dropped. + """ + # Count switches per group + diffs = cell.groupby(group_col)[d_col].diff().abs() + switches_per_group = diffs.fillna(0).groupby(cell[group_col]).sum() + multi_switch_groups = switches_per_group[switches_per_group > 1].index.tolist() + n_dropped = len(multi_switch_groups) + if n_dropped > 0: + warnings.warn( + f"drop_larger_lower=True dropped {n_dropped} multi-switch group(s) " + f"matching R DIDmultiplegtDYN behavior. Examples: " + f"{multi_switch_groups[:5]}" + + (f" (and {n_dropped - 5} more)" if n_dropped > 5 else ""), + UserWarning, + stacklevel=3, + ) + cell = cell[~cell[group_col].isin(multi_switch_groups)].reset_index(drop=True) + return cell, n_dropped + + +def _compute_per_period_dids( + D_mat: np.ndarray, + Y_mat: np.ndarray, + N_mat: np.ndarray, + periods: List[Any], +) -> Tuple[ + Dict[Any, Dict[str, Any]], + List[str], + np.ndarray, + np.ndarray, + np.ndarray, + np.ndarray, + np.ndarray, + np.ndarray, + np.ndarray, + np.ndarray, +]: + """ + Compute per-period DID_+,t and DID_-,t with explicit A11 zero-retention. + + Returns + ------- + per_period_effects : dict + Keyed by period; values are full per-period dicts including the + ``did_*_t_a11_zeroed`` flags. + a11_warnings : list of str + One string per period that triggered an A11 violation. + did_plus_t_arr : np.ndarray + DID_+,t values aligned to ``periods[1:]``. + did_minus_t_arr : np.ndarray + DID_-,t values aligned to ``periods[1:]``. + n_10_t_arr : np.ndarray + Joiner cell counts aligned to ``periods[1:]``. + n_01_t_arr : np.ndarray + Leaver cell counts aligned to ``periods[1:]``. + n_00_t_arr : np.ndarray + Stable-untreated cell counts aligned to ``periods[1:]``. + n_11_t_arr : np.ndarray + Stable-treated cell counts aligned to ``periods[1:]``. + a11_plus_zeroed_arr : np.ndarray + Boolean flags marking periods where DID_+,t was zeroed by the + A11 convention (joiners present but no stable_0 controls). + a11_minus_zeroed_arr : np.ndarray + Mirror for DID_-,t. + """ + n_periods = len(periods) + per_period_effects: Dict[Any, Dict[str, Any]] = {} + a11_warnings: List[str] = [] + did_plus_t_list: List[float] = [] + did_minus_t_list: List[float] = [] + n_10_t_list: List[int] = [] + n_01_t_list: List[int] = [] + n_00_t_list: List[int] = [] + n_11_t_list: List[int] = [] + a11_plus_zeroed_list: List[bool] = [] + a11_minus_zeroed_list: List[bool] = [] + + for t_idx in range(1, n_periods): + d_curr = D_mat[:, t_idx] + d_prev = D_mat[:, t_idx - 1] + y_curr = Y_mat[:, t_idx] + y_prev = Y_mat[:, t_idx - 1] + n_curr = N_mat[:, t_idx] + + # Cell-presence guard: a (g, t) cell only counts if BOTH t and t-1 + # were observed for that group (n_gt > 0 and n_{g,t-1} > 0). + n_prev = N_mat[:, t_idx - 1] + present = (n_curr > 0) & (n_prev > 0) + + joiner_mask = (d_prev == 0) & (d_curr == 1) & present + stable0_mask = (d_prev == 0) & (d_curr == 0) & present + leaver_mask = (d_prev == 1) & (d_curr == 0) & present + stable1_mask = (d_prev == 1) & (d_curr == 1) & present + + # AER 2020 Theorem 3 N_{a,b,t} weights are CELL counts, not + # within-cell observation sums. Each (g, t) cell contributes once + # regardless of how many original observations fed into the + # y_gt cell mean. See REGISTRY.md ChaisemartinDHaultfoeuille + # estimator equations. + n_10 = int(joiner_mask.sum()) + n_00 = int(stable0_mask.sum()) + n_01 = int(leaver_mask.sum()) + n_11 = int(stable1_mask.sum()) + + # --- DID_+,t (joiners side) --- + did_plus_t_a11_zeroed = False + if n_10 == 0: + did_plus_t = 0.0 + elif n_00 == 0: + # A11 violation: joiners exist but no stable_0 controls + did_plus_t = 0.0 + did_plus_t_a11_zeroed = True + a11_warnings.append(f"period {periods[t_idx]}: joiners present, no stable_0") + else: + # Unweighted means over cells (each cell contributes equally) + joiner_avg = float((y_curr[joiner_mask] - y_prev[joiner_mask]).mean()) + stable0_avg = float((y_curr[stable0_mask] - y_prev[stable0_mask]).mean()) + did_plus_t = joiner_avg - stable0_avg + + # --- DID_-,t (leavers side) --- + did_minus_t_a11_zeroed = False + if n_01 == 0: + did_minus_t = 0.0 + elif n_11 == 0: + did_minus_t = 0.0 + did_minus_t_a11_zeroed = True + a11_warnings.append(f"period {periods[t_idx]}: leavers present, no stable_1") + else: + stable1_avg = float((y_curr[stable1_mask] - y_prev[stable1_mask]).mean()) + leaver_avg = float((y_curr[leaver_mask] - y_prev[leaver_mask]).mean()) + did_minus_t = stable1_avg - leaver_avg + + per_period_effects[periods[t_idx]] = { + "did_plus_t": did_plus_t, + "did_minus_t": did_minus_t, + "n_10_t": n_10, + "n_01_t": n_01, + "n_00_t": n_00, + "n_11_t": n_11, + "did_plus_t_a11_zeroed": did_plus_t_a11_zeroed, + "did_minus_t_a11_zeroed": did_minus_t_a11_zeroed, + } + did_plus_t_list.append(did_plus_t) + did_minus_t_list.append(did_minus_t) + n_10_t_list.append(n_10) + n_01_t_list.append(n_01) + n_00_t_list.append(n_00) + n_11_t_list.append(n_11) + a11_plus_zeroed_list.append(did_plus_t_a11_zeroed) + a11_minus_zeroed_list.append(did_minus_t_a11_zeroed) + + return ( + per_period_effects, + a11_warnings, + np.array(did_plus_t_list, dtype=float), + np.array(did_minus_t_list, dtype=float), + np.array(n_10_t_list, dtype=int), + np.array(n_01_t_list, dtype=int), + np.array(n_00_t_list, dtype=int), + np.array(n_11_t_list, dtype=int), + np.array(a11_plus_zeroed_list, dtype=bool), + np.array(a11_minus_zeroed_list, dtype=bool), + ) + + +def _compute_placebo( + D_mat: np.ndarray, + Y_mat: np.ndarray, + N_mat: np.ndarray, + periods: List[Any], +) -> Optional[Tuple[float, bool, List[str]]]: + """ + Compute the single-lag placebo DID_M^pl from AER 2020 placebo specification. + + Same logic as DID_M but evaluated on the pre-event difference + ``Y_{g, t-1} - Y_{g, t-2}`` for cells with three-period histories. + Requires ``T >= 3``. + + Mirrors the main path's A11 zero-retention machinery: when placebo + joiners exist but no 3-period stable_0 controls do (or symmetric + for leavers/stable_1), the affected per-period contribution is set + to zero AND a warning string is appended to ``placebo_a11_warnings``. + The caller is responsible for surfacing the consolidated warning. + The zero-retention preserves the period's switcher count in the + placebo ``N_S^pl`` denominator, biasing the placebo toward zero in + the offending direction (matching placebo paper convention). + + Returns + ------- + None if ``T < 3`` or no qualifying cells. Otherwise a tuple + ``(placebo_effect, True, placebo_a11_warnings)`` where + ``placebo_a11_warnings`` is a list of one string per period that + triggered an A11 violation in the placebo numerator. + """ + n_periods = len(periods) + if n_periods < 3: + return None + + placebo_plus_per_t: List[float] = [] + placebo_minus_per_t: List[float] = [] + n_10_per_t: List[int] = [] + n_01_per_t: List[int] = [] + placebo_a11_warnings: List[str] = [] + + for t_idx in range(2, n_periods): + d_curr = D_mat[:, t_idx] + d_prev = D_mat[:, t_idx - 1] + d_pre_prev = D_mat[:, t_idx - 2] + y_prev = Y_mat[:, t_idx - 1] + y_pre_prev = Y_mat[:, t_idx - 2] + + # Cell-presence guard: a (g, t) cell only counts if all three + # consecutive periods (t-2, t-1, t) were observed for the group. + present = (N_mat[:, t_idx] > 0) & (N_mat[:, t_idx - 1] > 0) & (N_mat[:, t_idx - 2] > 0) + + # Joiners that have a 3-period history with stable D=0 in t-2 and t-1 + joiner_mask = (d_pre_prev == 0) & (d_prev == 0) & (d_curr == 1) & present + # Stable_0 controls with stable D=0 in t-2 and t-1 + stable0_mask = (d_pre_prev == 0) & (d_prev == 0) & (d_curr == 0) & present + # Mirror for leavers/stable_1 (3-period stable treatment then leave) + leaver_mask = (d_pre_prev == 1) & (d_prev == 1) & (d_curr == 0) & present + stable1_mask = (d_pre_prev == 1) & (d_prev == 1) & (d_curr == 1) & present + + # Placebo weights are CELL counts (matching Theorem 3 convention) + n_10 = int(joiner_mask.sum()) + n_00 = int(stable0_mask.sum()) + n_01 = int(leaver_mask.sum()) + n_11 = int(stable1_mask.sum()) + + # Joiners side: distinguish "no joiners" (natural zero) from + # "joiners but no stable_0" (A11 violation, flagged + warned) + if n_10 == 0: + placebo_plus_t = 0.0 + elif n_00 == 0: + placebo_plus_t = 0.0 + placebo_a11_warnings.append( + f"period {periods[t_idx]}: placebo joiners present, no stable_0" + ) + else: + joiner_avg = float((y_prev[joiner_mask] - y_pre_prev[joiner_mask]).mean()) + stable0_avg = float((y_prev[stable0_mask] - y_pre_prev[stable0_mask]).mean()) + placebo_plus_t = joiner_avg - stable0_avg + + # Leavers side: symmetric A11 distinction + if n_01 == 0: + placebo_minus_t = 0.0 + elif n_11 == 0: + placebo_minus_t = 0.0 + placebo_a11_warnings.append( + f"period {periods[t_idx]}: placebo leavers present, no stable_1" + ) + else: + stable1_avg = float((y_prev[stable1_mask] - y_pre_prev[stable1_mask]).mean()) + leaver_avg = float((y_prev[leaver_mask] - y_pre_prev[leaver_mask]).mean()) + placebo_minus_t = stable1_avg - leaver_avg + + placebo_plus_per_t.append(placebo_plus_t) + placebo_minus_per_t.append(placebo_minus_t) + n_10_per_t.append(n_10) + n_01_per_t.append(n_01) + + n_10_arr = np.array(n_10_per_t, dtype=int) + n_01_arr = np.array(n_01_per_t, dtype=int) + N_S_pl = int(n_10_arr.sum() + n_01_arr.sum()) + if N_S_pl == 0: + return None + placebo_effect = float( + (n_10_arr @ np.array(placebo_plus_per_t) + n_01_arr @ np.array(placebo_minus_per_t)) + / N_S_pl + ) + return placebo_effect, True, placebo_a11_warnings + + +def _compute_full_per_group_contributions( + D_mat: np.ndarray, + Y_mat: np.ndarray, + N_mat: np.ndarray, + n_10_t_arr: np.ndarray, + n_00_t_arr: np.ndarray, + n_01_t_arr: np.ndarray, + n_11_t_arr: np.ndarray, + a11_plus_zeroed_arr: np.ndarray, + a11_minus_zeroed_arr: np.ndarray, + side: str = "overall", +) -> np.ndarray: + """ + Compute the per-group influence function ``U^G_g`` for ``DID_M``, + ``DID_+``, or ``DID_-`` by summing role-weighted outcome differences + across all periods (full ``Lambda^G_{g,l=1}`` from Section 3.7.2 of + the dynamic companion paper, evaluated at horizon ``l = 1``). + + Decomposition (for ``side='overall'``):: + + N_S * DID_M = sum_t [ + sum_{g in joiners(t)} (Y_{g,t} - Y_{g,t-1}) + - (n_10_t / n_00_t) * sum_{g in stable_0(t)} (Y_{g,t} - Y_{g,t-1}) + + (n_01_t / n_11_t) * sum_{g in stable_1(t)} (Y_{g,t} - Y_{g,t-1}) + - sum_{g in leavers(t)} (Y_{g,t} - Y_{g,t-1}) + ] + + Each ``(g, t)`` cell contributes to ``U^G_g`` once per period, with + the role weight determined by its ``(D_{g,t-1}, D_{g,t})`` transition. + A switching group typically contributes from MULTIPLE periods (its + own switch period + every period where it serves as a stable + control); a never-switching group contributes only via its stable- + control roles (which can be non-zero when it serves as a control + for other cohorts' switches). + + Periods where ``DID_+,t`` or ``DID_-,t`` were zeroed under the A11 + convention contribute zero on the affected side, matching the + point estimate. + + Parameters + ---------- + D_mat, Y_mat, N_mat : np.ndarray of shape (n_groups, n_periods) + Pivoted treatment, outcome, and observation-count matrices. + n_10_t_arr, n_00_t_arr, n_01_t_arr, n_11_t_arr : np.ndarray + Per-period CELL counts aligned to ``periods[1:]``. + a11_plus_zeroed_arr, a11_minus_zeroed_arr : np.ndarray of bool + Per-period A11-zeroing flags aligned to ``periods[1:]``. + side : {"overall", "joiners", "leavers"} + Which contribution to compute: + + - ``"overall"``: returns ``U^G_g`` such that ``U.sum() == N_S * DID_M`` + - ``"joiners"``: returns ``U^G_g`` such that ``U.sum() == joiner_total * DID_+`` + (only the joiners + stable_0 terms) + - ``"leavers"``: returns ``U^G_g`` such that ``U.sum() == leaver_total * DID_-`` + (only the leavers + stable_1 terms, with the leavers side's sign convention) + + Returns + ------- + U : np.ndarray of shape (n_groups,) + Per-group contributions. NOT cohort-centered; the caller is + responsible for centering before computing the SE. + """ + if side not in ("overall", "joiners", "leavers"): + raise ValueError(f"side must be one of overall/joiners/leavers, got {side!r}") + + n_groups, n_periods = D_mat.shape + U = np.zeros(n_groups, dtype=float) + + include_joiners_side = side in ("overall", "joiners") + include_leavers_side = side in ("overall", "leavers") + + for t_idx in range(1, n_periods): + d_curr = D_mat[:, t_idx] + d_prev = D_mat[:, t_idx - 1] + y_diff = Y_mat[:, t_idx] - Y_mat[:, t_idx - 1] + n_curr = N_mat[:, t_idx] + n_prev = N_mat[:, t_idx - 1] + present = (n_curr > 0) & (n_prev > 0) + + joiner_mask = (d_prev == 0) & (d_curr == 1) & present + stable0_mask = (d_prev == 0) & (d_curr == 0) & present + leaver_mask = (d_prev == 1) & (d_curr == 0) & present + stable1_mask = (d_prev == 1) & (d_curr == 1) & present + + n_10_t = int(n_10_t_arr[t_idx - 1]) + n_00_t = int(n_00_t_arr[t_idx - 1]) + n_01_t = int(n_01_t_arr[t_idx - 1]) + n_11_t = int(n_11_t_arr[t_idx - 1]) + + # Joiners side (+y_diff for joiners; -(n_10/n_00)*y_diff for stable_0) + if ( + include_joiners_side + and not bool(a11_plus_zeroed_arr[t_idx - 1]) + and n_10_t > 0 + and n_00_t > 0 + ): + U[joiner_mask] += y_diff[joiner_mask] + U[stable0_mask] -= (n_10_t / n_00_t) * y_diff[stable0_mask] + + # Leavers side (-y_diff for leavers; +(n_01/n_11)*y_diff for stable_1) + if ( + include_leavers_side + and not bool(a11_minus_zeroed_arr[t_idx - 1]) + and n_01_t > 0 + and n_11_t > 0 + ): + U[leaver_mask] -= y_diff[leaver_mask] + U[stable1_mask] += (n_01_t / n_11_t) * y_diff[stable1_mask] + + return U + + +def _cohort_recenter( + U: np.ndarray, + cohort_ids: np.ndarray, +) -> np.ndarray: + """ + Subtract cohort-conditional means from U. + + For each cohort id, computes ``U_bar_k = mean(U[cohort==k])`` and + returns ``U - U_bar_{cohort(g)}``. This is the per-group cohort- + recentering step from Web Appendix Section 3.7.3 of the dynamic + companion paper. Critical: subtracts the cohort mean, NOT a single + grand mean — using a grand mean silently produces a smaller, + incorrect variance. + """ + U_centered = U.astype(float).copy() + if U.size == 0: + return U_centered + unique_cohorts = np.unique(cohort_ids) + for k in unique_cohorts: + in_cohort = cohort_ids == k + if in_cohort.any(): + U_centered[in_cohort] = U[in_cohort] - U[in_cohort].mean() + return U_centered + + +def _compute_cohort_recentered_inputs( + D_mat: np.ndarray, + Y_mat: np.ndarray, + N_mat: np.ndarray, + n_10_t_arr: np.ndarray, + n_00_t_arr: np.ndarray, + n_01_t_arr: np.ndarray, + n_11_t_arr: np.ndarray, + a11_plus_zeroed_arr: np.ndarray, + a11_minus_zeroed_arr: np.ndarray, + all_groups: List[Any], + singleton_baseline_groups: List[Any], +) -> Tuple[np.ndarray, int, int, int, np.ndarray, np.ndarray]: + """ + Compute the cohort-centered influence-function vectors for variance. + + Implements the full ``Lambda^G_{g,l=1}`` weight vector from + Section 3.7.2 of the dynamic companion paper (NBER WP 29873) at + horizon ``l = 1``: each group's per-period role weights (joiner, + stable_0, leaver, stable_1) sum to a per-group ``U^G_g`` value + that, summed across groups, recovers ``N_S * DID_M``. + + Cohorts are defined by the triple ``(D_{g,1}, F_g, S_g)`` where + ``F_g`` is the first switch period and ``S_g`` is the switch + direction (+1 joiner, -1 leaver, 0 never-switching). Never- + switching groups form their own cohorts indexed by baseline only. + + Per footnote 15 of the dynamic paper (passed in via + ``singleton_baseline_groups``), groups whose baseline ``D_{g,1}`` + value is unique in the post-drop panel have no cohort peer and are + excluded from the variance computation only. They remain in the + point-estimate sample as period-based stable controls (this + matches Python's documented period-vs-cohort stable-control + interpretation; the cell DataFrame entering ``_compute_per_period_dids`` + retains them). + + Returns + ------- + U_centered_overall : np.ndarray + Cohort-centered IF vector for ``DID_M`` over the variance- + eligible groups (post-singleton-filter). + n_groups_for_overall : int + ``U_centered_overall.size`` for sanity-checking by the caller. + n_cohorts : int + Distinct cohorts in the variance-eligible group set. + n_groups_dropped_never_switching : int + Count of never-switching groups for results metadata. (They + ARE included in the variance computation under the full IF + formula because they can have non-zero contributions when + serving as stable controls; this count is reported for + backwards compatibility with the existing results dataclass + field but no longer represents an actual exclusion.) + U_centered_joiners : np.ndarray + Cohort-centered IF vector for ``DID_+`` (joiners-only side). + U_centered_leavers : np.ndarray + Cohort-centered IF vector for ``DID_-`` (leavers-only side). + """ + n_groups, n_periods = D_mat.shape + + if n_groups == 0: + return ( + np.array([], dtype=float), + 0, + 0, + 0, + np.array([], dtype=float), + np.array([], dtype=float), + ) + + # Per-group baseline, first switch time, switch direction. + # + # Defensive: even though fit() Step 5a rejects groups missing the + # first global period and drops groups with interior gaps, this + # helper might also be called from other code paths in the future. + # We assert no NaN baselines (would catch a fit() validation + # regression) and gate first-switch detection on N_mat presence so + # missing intermediate periods can't be misread as switches. + if N_mat.size > 0 and (N_mat[:, 0] <= 0).any(): + raise ValueError( + "_compute_cohort_recentered_inputs: at least one group is missing " + "the first global period in N_mat. fit() Step 5a should have " + "rejected this — if you are calling this helper directly, ensure " + "every group has an observation at the first global period." + ) + baselines = D_mat[:, 0].astype(int) + first_switch_idx = np.full(n_groups, -1, dtype=int) + switch_direction = np.zeros(n_groups, dtype=int) # +1 joiner, -1 leaver, 0 never-switching + + for g in range(n_groups): + for t in range(1, n_periods): + # Both periods must be observed for the transition to be valid + if N_mat[g, t] <= 0 or N_mat[g, t - 1] <= 0: + continue + if D_mat[g, t] != D_mat[g, t - 1]: + first_switch_idx[g] = t + switch_direction[g] = 1 if D_mat[g, t] > D_mat[g, t - 1] else -1 + break + + n_groups_dropped_never_switching = int((switch_direction == 0).sum()) + + # Variance-eligibility mask: include all groups EXCEPT singleton- + # baseline groups (footnote 15) which have no cohort peer. + singleton_baseline_set = set(singleton_baseline_groups) + eligible_mask = np.array([g not in singleton_baseline_set for g in all_groups], dtype=bool) + + # Cohort identification: (D_{g,1}, F_g, S_g) triples for the + # variance-eligible group set. Never-switching groups (S_g = 0) + # have F_g = -1 and form cohorts indexed by baseline alone. + cohort_keys = [ + (int(baselines[g]), int(first_switch_idx[g]), int(switch_direction[g])) + for g in range(n_groups) + ] + unique_cohorts: Dict[Tuple[int, int, int], int] = {} + cohort_id = np.zeros(n_groups, dtype=int) + for g in range(n_groups): + if not eligible_mask[g]: + cohort_id[g] = -1 + continue + key = cohort_keys[g] + if key not in unique_cohorts: + unique_cohorts[key] = len(unique_cohorts) + cohort_id[g] = unique_cohorts[key] + n_cohorts = len(unique_cohorts) + + # Compute the full IF vectors via the new helper + U_overall_full = _compute_full_per_group_contributions( + D_mat=D_mat, + Y_mat=Y_mat, + N_mat=N_mat, + n_10_t_arr=n_10_t_arr, + n_00_t_arr=n_00_t_arr, + n_01_t_arr=n_01_t_arr, + n_11_t_arr=n_11_t_arr, + a11_plus_zeroed_arr=a11_plus_zeroed_arr, + a11_minus_zeroed_arr=a11_minus_zeroed_arr, + side="overall", + ) + U_joiners_full = _compute_full_per_group_contributions( + D_mat=D_mat, + Y_mat=Y_mat, + N_mat=N_mat, + n_10_t_arr=n_10_t_arr, + n_00_t_arr=n_00_t_arr, + n_01_t_arr=n_01_t_arr, + n_11_t_arr=n_11_t_arr, + a11_plus_zeroed_arr=a11_plus_zeroed_arr, + a11_minus_zeroed_arr=a11_minus_zeroed_arr, + side="joiners", + ) + U_leavers_full = _compute_full_per_group_contributions( + D_mat=D_mat, + Y_mat=Y_mat, + N_mat=N_mat, + n_10_t_arr=n_10_t_arr, + n_00_t_arr=n_00_t_arr, + n_01_t_arr=n_01_t_arr, + n_11_t_arr=n_11_t_arr, + a11_plus_zeroed_arr=a11_plus_zeroed_arr, + a11_minus_zeroed_arr=a11_minus_zeroed_arr, + side="leavers", + ) + + # Restrict to variance-eligible groups (drop singleton-baseline groups) + U_overall = U_overall_full[eligible_mask] + U_joiners = U_joiners_full[eligible_mask] + U_leavers = U_leavers_full[eligible_mask] + cohort_id_eligible = cohort_id[eligible_mask] + + # Cohort-recenter each IF vector + U_centered_overall = _cohort_recenter(U_overall, cohort_id_eligible) + U_centered_joiners = _cohort_recenter(U_joiners, cohort_id_eligible) + U_centered_leavers = _cohort_recenter(U_leavers, cohort_id_eligible) + + return ( + U_centered_overall, + U_centered_overall.size, + n_cohorts, + n_groups_dropped_never_switching, + U_centered_joiners, + U_centered_leavers, + ) + + +def _plugin_se(U_centered: np.ndarray, divisor: int) -> float: + """ + Compute the cohort-recentered plug-in standard error. + + Implements ``SE = sqrt(sum_g U_centered[g]^2 / N_l) / sqrt(N_l)``, + which is the simplified form of Section 3.7.3's plug-in formula + after the cohort recentering has been applied to ``U_centered``. + + The plain ``(1/N_l) * sum_g U_centered^2 / N_l`` form gives the + variance; we take its square root for the SE. + + Returns ``NaN`` in three degenerate cases: + + 1. ``U_centered`` is empty (no variance-eligible groups). + 2. ``divisor <= 0`` (no switching cells in N_S). + 3. ``sum(U_centered**2) <= 0`` — every cohort is a singleton, so + cohort recentering produces an identically-zero centered IF + vector and the variance is unidentified. The caller should + detect this case (NaN return + non-empty input) and emit a + user-facing warning explaining the degenerate-cohort condition. + Returning ``NaN`` rather than ``0.0`` prevents the silently + implies-infinite-precision failure mode. + """ + n = U_centered.size + if n == 0 or divisor <= 0: + return float("nan") + sum_sq = float((U_centered**2).sum()) + if sum_sq <= 0: + # Degenerate-cohort case: every cohort is a singleton, so + # cohort recentering produces all zeros. The variance is + # unidentified — return NaN rather than 0.0 so downstream + # inference is NaN-consistent and the caller surfaces a + # warning. See the **Note** in REGISTRY.md + # ChaisemartinDHaultfoeuille. + return float("nan") + sigma_hat_sq = sum_sq / divisor + if not np.isfinite(sigma_hat_sq) or sigma_hat_sq < 0: + return float("nan") + return float(np.sqrt(sigma_hat_sq) / np.sqrt(divisor)) + + +def _build_group_time_design( + cell: pd.DataFrame, + group_col: str, + time_col: str, +) -> Tuple[np.ndarray, List[str]]: + """ + Build a dense (intercept + group dummies + time dummies) design matrix. + + Used by the TWFE decomposition diagnostic. The first group and first + period are dropped as the reference categories. Returns the matrix + and a list of column names. + """ + if cell.empty: + raise ValueError( + "Cannot compute TWFE diagnostic on an empty cell DataFrame. " + "Provide a panel with at least 2 groups and 2 time periods." + ) + groups = sorted(cell[group_col].unique().tolist()) + times = sorted(cell[time_col].unique().tolist()) + n = len(cell) + n_groups = len(groups) + n_times = len(times) + if n_groups < 2 or n_times < 2: + raise ValueError( + f"TWFE diagnostic requires at least 2 groups and 2 time periods, " + f"got {n_groups} group(s) and {n_times} period(s)." + ) + + # Columns: [intercept, group_1, ..., group_{G-1}, time_1, ..., time_{T-1}] + n_cols = 1 + (n_groups - 1) + (n_times - 1) + X = np.zeros((n, n_cols), dtype=float) + X[:, 0] = 1.0 # intercept + + group_to_col = {g: 1 + i for i, g in enumerate(groups[1:])} + time_to_col = {t: 1 + (n_groups - 1) + i for i, t in enumerate(times[1:])} + + group_arr = cell[group_col].to_numpy() + time_arr = cell[time_col].to_numpy() + for i in range(n): + g = group_arr[i] + t = time_arr[i] + if g in group_to_col: + X[i, group_to_col[g]] = 1.0 + if t in time_to_col: + X[i, time_to_col[t]] = 1.0 + + column_names = ( + ["intercept"] + [f"group[{g}]" for g in groups[1:]] + [f"time[{t}]" for t in times[1:]] + ) + return X, column_names + + +def _compute_twfe_diagnostic( + cell: pd.DataFrame, + group_col: str, + time_col: str, + rank_deficient_action: str, +) -> TWFEWeightsResult: + """ + Compute the per-cell TWFE decomposition diagnostic from Theorem 1 of AER 2020. + + Steps: + + 1. Regress ``d_gt`` on group + time fixed effects via :func:`solve_ols`. + 2. Compute residuals ``eps_{g, t}`` from the regression. + 3. Compute per-cell **contribution weights** (the Theorem 1 + decomposition object): + ``cw_{g,t} = N_{g,t} * eps_{g,t} / sum_{treated} N * eps`` + These are exported in the ``weights`` column of the returned + ``TWFEWeightsResult``. + 4. Count negative contribution weights among treated cells. + 5. Compute the plain TWFE coefficient as a separate regression of + ``y_gt`` on the same FE plus the treatment indicator. + 6. Compute ``sigma_fe`` from the Corollary 1 **paper weights** + (a distinct object from the contribution weights): + ``w_paper = eps / sum_treated(s * eps)`` where + ``s = N_{g,t} / N_1`` are observation shares. The paper weight + is centered at 1 under observation-share weighting. Then: + ``sigma_fe = |beta_fe| / sqrt(sum_treated(s * (w_paper - 1)^2))`` + which is the smallest standard deviation of cell-level treatment + effects that could flip the sign of the plain TWFE estimator. + """ + X, _ = _build_group_time_design(cell, group_col, time_col) + d_arr = cell["d_gt"].to_numpy().astype(float) + n_arr = cell["n_gt"].to_numpy().astype(float) + y_arr = cell["y_gt"].to_numpy().astype(float) + + # Step 1-2: regress d on FE + coef_d, residuals_d, _ = solve_ols( + X, + d_arr, + return_vcov=False, + rank_deficient_action=rank_deficient_action, + weights=n_arr, + ) + eps = residuals_d + + # Step 3: per-cell weights — normalize by sum over treated cells + treated_mask = d_arr == 1 + denom = float((n_arr[treated_mask] * eps[treated_mask]).sum()) + if denom == 0: + # Cannot normalize: the design has zero treated mass after FE absorption. + # Warn so the user knows the diagnostic returned NaN values rather than + # silently substituting them. + warnings.warn( + "TWFE decomposition diagnostic could not normalize per-cell " + "weights: the sum of N_{g,t} * residual over treated cells is " + "zero. This typically means the design matrix has perfect " + "collinearity between treatment and the group/period fixed " + "effects. Returning NaN for fraction_negative, sigma_fe, and " + "beta_fe.", + UserWarning, + stacklevel=3, + ) + weights_df = cell[[group_col, time_col]].copy() + weights_df["weight"] = 0.0 + return TWFEWeightsResult( + weights=weights_df, + fraction_negative=float("nan"), + sigma_fe=float("nan"), + beta_fe=float("nan"), + ) + w_gt = (n_arr * eps) / denom + + weights_df = cell[[group_col, time_col]].copy() + weights_df["weight"] = w_gt + + fraction_negative = float((w_gt[treated_mask] < 0).sum() / treated_mask.sum()) + + # Step 5: plain TWFE regression of y on (FE + d_gt) + X_with_d = np.column_stack([X, d_arr.reshape(-1, 1)]) + coef_fe, _, _ = solve_ols( + X_with_d, + y_arr, + return_vcov=False, + rank_deficient_action=rank_deficient_action, + weights=n_arr, + ) + beta_fe = float(coef_fe[-1]) + + # Step 6: sigma_fe per Corollary 1 of AER 2020 + # + # The paper defines w_{g,t} = eps_{g,t} / E_treated[eps], which + # is DIFFERENT from the contribution weights w_gt exported in the + # weights DataFrame (contribution_weight = s * w_paper). The paper + # weight has the property that sum(s * w_paper) = 1 (centered at + # 1 under observation-share weighting). sigma_fe uses the paper + # weight: + # + # w_paper = eps / sum_treated(s * eps) + # sigma(w) = sqrt(sum_treated(s * (w_paper - 1)^2)) + # sigma_fe = |beta_fe| / sigma(w) + # + # where s_{g,t} = N_{g,t} / N_1 are observation shares. + eps_treated = eps[treated_mask] + n_treated_arr = n_arr[treated_mask] + n1 = float(n_treated_arr.sum()) # total treated observations + if n1 > 0: + shares = n_treated_arr / n1 # s_{g,t} = N_{g,t} / N_1 + denom_paper = float((shares * eps_treated).sum()) + if abs(denom_paper) > 0: + w_paper = eps_treated / denom_paper # paper's w_{g,t} + # Weighted variance around 1 (the weighted mean of w_paper is 1 by construction) + var_w = float((shares * (w_paper - 1.0) ** 2).sum()) + else: + var_w = 0.0 + else: + var_w = 0.0 + if var_w > 0 and np.isfinite(beta_fe): + sigma_fe = float(abs(beta_fe) / np.sqrt(var_w)) + else: + sigma_fe = float("nan") + + return TWFEWeightsResult( + weights=weights_df, + fraction_negative=fraction_negative, + sigma_fe=sigma_fe, + beta_fe=beta_fe, + ) + + +# ============================================================================= +# Convenience functions +# ============================================================================= + + +def chaisemartin_dhaultfoeuille( + data: pd.DataFrame, + outcome: str, + group: str, + time: str, + treatment: str, + **kwargs: Any, +) -> ChaisemartinDHaultfoeuilleResults: + """ + One-shot convenience wrapper around + :class:`ChaisemartinDHaultfoeuille`. + + Equivalent to:: + + ChaisemartinDHaultfoeuille(**init_kwargs).fit( + data, outcome=..., group=..., time=..., treatment=..., + **fit_kwargs, + ) + + All keyword arguments are split between ``__init__`` and ``fit`` based + on which signature accepts them. Useful for one-line use in scripts. + + Parameters + ---------- + data : pd.DataFrame + outcome, group, time, treatment : str + **kwargs : Any + Forwarded to ``ChaisemartinDHaultfoeuille.__init__`` or + ``.fit()`` based on parameter name. + + Returns + ------- + ChaisemartinDHaultfoeuilleResults + """ + init_keys = { + "alpha", + "cluster", + "n_bootstrap", + "bootstrap_weights", + "seed", + "placebo", + "twfe_diagnostic", + "drop_larger_lower", + "rank_deficient_action", + } + init_kwargs = {k: v for k, v in kwargs.items() if k in init_keys} + fit_kwargs = {k: v for k, v in kwargs.items() if k not in init_keys} + est = ChaisemartinDHaultfoeuille(**init_kwargs) + return est.fit( + data, + outcome=outcome, + group=group, + time=time, + treatment=treatment, + **fit_kwargs, + ) + + +def twowayfeweights( + data: pd.DataFrame, + outcome: str, + group: str, + time: str, + treatment: str, + rank_deficient_action: str = "warn", +) -> TWFEWeightsResult: + """ + Standalone TWFE decomposition diagnostic. + + Computes the per-cell weights, fraction negative, and ``sigma_fe`` + from Theorem 1 of de Chaisemartin & D'Haultfoeuille (2020), without + fitting the full dCDH estimator. Mirrors the standalone Stata + ``twowayfeweights`` package. + + Parameters + ---------- + data : pd.DataFrame + Individual-level panel. + outcome : str + group : str + time : str + treatment : str + rank_deficient_action : str, default="warn" + Action when the FE design matrix is rank-deficient. + + Returns + ------- + TWFEWeightsResult + Object with attributes ``weights`` (DataFrame), ``fraction_negative`` + (float), ``sigma_fe`` (float), and ``beta_fe`` (float). + """ + # Validation + cell aggregation via the same helper used by + # ChaisemartinDHaultfoeuille.fit() — enforces NaN/binary/within-cell + # rules from REGISTRY.md so the standalone diagnostic does not + # silently mishandle malformed input. + cell = _validate_and_aggregate_to_cells( + data=data, + outcome=outcome, + group=group, + time=time, + treatment=treatment, + ) + return _compute_twfe_diagnostic( + cell=cell, + group_col=group, + time_col=time, + rank_deficient_action=rank_deficient_action, + ) diff --git a/diff_diff/chaisemartin_dhaultfoeuille_bootstrap.py b/diff_diff/chaisemartin_dhaultfoeuille_bootstrap.py new file mode 100644 index 000000000..eca610a6e --- /dev/null +++ b/diff_diff/chaisemartin_dhaultfoeuille_bootstrap.py @@ -0,0 +1,326 @@ +""" +Multiplier-bootstrap inference for the de Chaisemartin-D'Haultfoeuille (dCDH) +estimator. + +The dCDH papers prescribe only the analytical cohort-recentered plug-in +variance from Web Appendix Section 3.7.3 of the dynamic companion paper. +This module adds an opt-in multiplier bootstrap clustered at the group +level, matching the inference convention used by ``CallawaySantAnna``, +``ImputationDiD``, and ``TwoStageDiD``. The bootstrap is a library +extension, not a paper requirement, and is documented as such in +``REGISTRY.md``. + +The mixin operates on **pre-computed cohort-centered influence-function +values**: the main estimator class computes per-group ``U^G_g`` values +during the analytical variance calculation, recenters them by their +cohort means (using the ``(D_{g,1}, F_g, S_g)`` triple), and stores the +recentered vector. The bootstrap then multiplies this vector by random +multiplier weights (Rademacher / Mammen / Webb) and re-aggregates to +produce a bootstrap distribution per target. +""" + +import warnings +from typing import TYPE_CHECKING, Optional, Tuple + +import numpy as np + +from diff_diff.bootstrap_utils import ( + compute_effect_bootstrap_stats as _compute_effect_bootstrap_stats, +) +from diff_diff.bootstrap_utils import ( + generate_bootstrap_weights_batch as _generate_bootstrap_weights_batch, +) +from diff_diff.chaisemartin_dhaultfoeuille_results import DCDHBootstrapResults + +__all__ = ["ChaisemartinDHaultfoeuilleBootstrapMixin"] + + +class ChaisemartinDHaultfoeuilleBootstrapMixin: + """ + Bootstrap-inference mixin for ``ChaisemartinDHaultfoeuille``. + + Provides a single entry point ``_compute_dcdh_bootstrap`` that takes + pre-computed centered influence-function values for each estimand + target (overall ``DID_M``, joiners ``DID_+``, leavers ``DID_-``, + placebo ``DID_M^pl``) and returns a populated + :class:`DCDHBootstrapResults`. + + The mixin is pure (no instance state of its own); it only references + instance attributes from the main class via ``TYPE_CHECKING`` hints. + """ + + # --- Type hints for attributes accessed from the main class --- + n_bootstrap: int + bootstrap_weights: str + alpha: float + seed: Optional[int] + + if TYPE_CHECKING: # pragma: no cover + + def _placeholder(self) -> None: ... # silences mypy "no attributes" warnings + + def _compute_dcdh_bootstrap( + self, + n_groups_for_overall: int, + u_centered_overall: np.ndarray, + divisor_overall: int, + original_overall: float, + joiners_inputs: Optional[Tuple[np.ndarray, int, float]] = None, + leavers_inputs: Optional[Tuple[np.ndarray, int, float]] = None, + placebo_inputs: Optional[Tuple[np.ndarray, int, float]] = None, + ) -> DCDHBootstrapResults: + """ + Compute multiplier-bootstrap inference for all dCDH targets. + + Each target ``T`` is summarized by: + + - a centered influence-function vector of length equal to the + number of groups contributing to ``T`` + - a re-aggregation **divisor**, which is the *switching-cell* + count from the Theorem 3 weighting formula (NOT a group + count). For ``DID_M`` the divisor is ``N_S = sum_t (N_{1,0,t} + + N_{0,1,t})``; for ``DID_+`` it is ``sum_t N_{1,0,t}``; for + ``DID_-`` it is ``sum_t N_{0,1,t}``. See REGISTRY.md + ``ChaisemartinDHaultfoeuille`` for the cell-count weighting + contract. + - the original point estimate of ``T`` (used as the centering + point for the percentile p-value) + + For each target, this method: + + 1. Generates an ``(n_bootstrap, n_groups_target)`` matrix of + multiplier weights via + :func:`~diff_diff.bootstrap_utils.generate_bootstrap_weights_batch`, + where ``n_groups_target`` is the IF vector length (one + weight per contributing group). + 2. Computes the bootstrap distribution as + ``W @ u_centered / divisor`` (one bootstrap replicate per + row), where ``divisor`` is the switching-cell count + described above. Note: the weight matrix has one column per + contributing group, but the divisor is a cell count — the + two are different quantities (groups can contribute to + multiple cells across periods). + 3. Passes the distribution + the original point estimate through + :func:`~diff_diff.bootstrap_utils.compute_effect_bootstrap_stats` + to obtain ``(SE, CI, p_value)``. + + Parameters + ---------- + n_groups_for_overall : int + Number of groups contributing to the overall ``DID_M`` + (length of ``u_centered_overall``). Used for shape + validation and weight-matrix sizing. + u_centered_overall : np.ndarray + Cohort-centered per-group influence-function values for + ``DID_M``. Shape: ``(n_groups_for_overall,)``. + divisor_overall : int + Re-aggregation **divisor** for ``DID_M`` — the switching- + cell count ``N_S = sum_t (N_{1,0,t} + N_{0,1,t})`` from + Theorem 3 of AER 2020. NOT a group count. For Phase 1 + this is the same value used in the analytical SE plug-in. + original_overall : float + The original point estimate of ``DID_M``. Used by + :func:`compute_effect_bootstrap_stats` for the percentile + p-value computation. + joiners_inputs : tuple, optional + ``(u_centered, divisor, original_effect)`` triple for the + joiners-only ``DID_+`` target. The ``divisor`` is the + joiner switching-cell total ``sum_t N_{1,0,t}``, NOT the + joiner group count. ``None`` when no joiners exist. + leavers_inputs : tuple, optional + Same triple for the leavers-only ``DID_-`` target. The + ``divisor`` is the leaver switching-cell total + ``sum_t N_{0,1,t}``. + placebo_inputs : tuple, optional + Same triple for the placebo ``DID_M^pl`` target. Always + ``None`` in Phase 1 — see REGISTRY.md placebo-bootstrap- + deferred Note. + + Returns + ------- + DCDHBootstrapResults + Populated bootstrap-results dataclass. Fields for unavailable + targets (joiners / leavers / placebo) are ``None``. + """ + if self.n_bootstrap <= 0: + raise ValueError( + f"_compute_dcdh_bootstrap called with n_bootstrap={self.n_bootstrap}; " + "must be > 0." + ) + if u_centered_overall.ndim != 1: + raise ValueError( + "u_centered_overall must be a 1-D array of per-group influence " + f"function values, got shape {u_centered_overall.shape}" + ) + if u_centered_overall.shape[0] != n_groups_for_overall: + raise ValueError( + f"u_centered_overall length ({u_centered_overall.shape[0]}) does not " + f"match n_groups_for_overall ({n_groups_for_overall})" + ) + if divisor_overall <= 0: + warnings.warn( + f"_compute_dcdh_bootstrap: divisor_overall={divisor_overall} <= 0; " + "returning all-NaN bootstrap results.", + RuntimeWarning, + stacklevel=2, + ) + return _empty_bootstrap_results(self.n_bootstrap, self.bootstrap_weights, self.alpha) + + rng = np.random.default_rng(self.seed) + + # --- Overall DID_M --- + overall_se, overall_ci, overall_p, overall_dist = _bootstrap_one_target( + u_centered=u_centered_overall, + divisor=divisor_overall, + original=original_overall, + n_bootstrap=self.n_bootstrap, + weight_type=self.bootstrap_weights, + alpha=self.alpha, + rng=rng, + context="dCDH overall DID_M bootstrap", + return_distribution=True, + ) + + results = DCDHBootstrapResults( + n_bootstrap=self.n_bootstrap, + weight_type=self.bootstrap_weights, + alpha=self.alpha, + overall_se=overall_se, + overall_ci=overall_ci, + overall_p_value=overall_p, + bootstrap_distribution=overall_dist, + ) + + # --- Joiners (DID_+) --- + if joiners_inputs is not None: + u_j, n_j, eff_j = joiners_inputs + if u_j.size > 0 and n_j > 0: + se_j, ci_j, p_j, _ = _bootstrap_one_target( + u_centered=u_j, + divisor=n_j, + original=eff_j, + n_bootstrap=self.n_bootstrap, + weight_type=self.bootstrap_weights, + alpha=self.alpha, + rng=rng, + context="dCDH joiners DID_+ bootstrap", + return_distribution=False, + ) + results.joiners_se = se_j + results.joiners_ci = ci_j + results.joiners_p_value = p_j + + # --- Leavers (DID_-) --- + if leavers_inputs is not None: + u_l, n_l, eff_l = leavers_inputs + if u_l.size > 0 and n_l > 0: + se_l, ci_l, p_l, _ = _bootstrap_one_target( + u_centered=u_l, + divisor=n_l, + original=eff_l, + n_bootstrap=self.n_bootstrap, + weight_type=self.bootstrap_weights, + alpha=self.alpha, + rng=rng, + context="dCDH leavers DID_- bootstrap", + return_distribution=False, + ) + results.leavers_se = se_l + results.leavers_ci = ci_l + results.leavers_p_value = p_l + + # --- Placebo (DID_M^pl) --- + if placebo_inputs is not None: + u_pl, n_pl, eff_pl = placebo_inputs + if u_pl.size > 0 and n_pl > 0: + se_pl, ci_pl, p_pl, _ = _bootstrap_one_target( + u_centered=u_pl, + divisor=n_pl, + original=eff_pl, + n_bootstrap=self.n_bootstrap, + weight_type=self.bootstrap_weights, + alpha=self.alpha, + rng=rng, + context="dCDH placebo DID_M^pl bootstrap", + return_distribution=False, + ) + results.placebo_se = se_pl + results.placebo_ci = ci_pl + results.placebo_p_value = p_pl + + return results + + +# ============================================================================= +# Internal helpers +# ============================================================================= + + +def _bootstrap_one_target( + u_centered: np.ndarray, + divisor: int, + original: float, + n_bootstrap: int, + weight_type: str, + alpha: float, + rng: np.random.Generator, + context: str, + return_distribution: bool, +) -> Tuple[float, Tuple[float, float], float, Optional[np.ndarray]]: + """ + Run the multiplier bootstrap for a single dCDH target. + + Generates an ``(n_bootstrap, len(u_centered))`` matrix of multiplier + weights, multiplies by ``u_centered``, and divides by ``divisor`` to + get a bootstrap distribution. Returns + ``(se, (ci_lo, ci_hi), p_value, distribution)``; ``distribution`` is + ``None`` when ``return_distribution=False`` (saves memory for + auxiliary targets). + + The "centered" naming is important: this function expects + ``u_centered`` to already have its cohort means subtracted (so the + sample mean of the bootstrap distribution should be approximately + zero, not the original effect). The original effect is passed + separately as the centering point for the percentile p-value. + """ + n_groups_target = u_centered.shape[0] + if n_groups_target == 0 or divisor == 0: + return np.nan, (np.nan, np.nan), np.nan, None + + weight_matrix = _generate_bootstrap_weights_batch( + n_bootstrap=n_bootstrap, + n_units=n_groups_target, + weight_type=weight_type, + rng=rng, + ) + + # Each bootstrap replicate: (1 / divisor) * sum_g w_b[g] * u_centered[g] + # The result is the bootstrap analog of the *deviation* from the original + # effect; shift it by `original` so the bootstrap distribution is centered + # at the original point estimate (which is what compute_effect_bootstrap_stats + # expects when computing the percentile p-value). + deviations = (weight_matrix @ u_centered) / divisor + boot_dist = original + deviations + + se, ci, p_value = _compute_effect_bootstrap_stats( + original_effect=original, + boot_dist=boot_dist, + alpha=alpha, + context=context, + ) + + return se, ci, p_value, (boot_dist if return_distribution else None) + + +def _empty_bootstrap_results( + n_bootstrap: int, weight_type: str, alpha: float +) -> DCDHBootstrapResults: + """Return an all-NaN bootstrap results object as a graceful fallback.""" + return DCDHBootstrapResults( + n_bootstrap=n_bootstrap, + weight_type=weight_type, + alpha=alpha, + overall_se=np.nan, + overall_ci=(np.nan, np.nan), + overall_p_value=np.nan, + ) diff --git a/diff_diff/chaisemartin_dhaultfoeuille_results.py b/diff_diff/chaisemartin_dhaultfoeuille_results.py new file mode 100644 index 000000000..b9ec92343 --- /dev/null +++ b/diff_diff/chaisemartin_dhaultfoeuille_results.py @@ -0,0 +1,818 @@ +""" +Result containers for the de Chaisemartin-D'Haultfoeuille (dCDH) estimator. + +This module contains ``ChaisemartinDHaultfoeuilleResults`` and +``DCDHBootstrapResults`` dataclasses produced by the +``ChaisemartinDHaultfoeuille`` (alias ``DCDH``) estimator. The dCDH +estimator is the only modern DiD estimator in the library that handles +non-absorbing (reversible) treatments. Phase 1 ships the contemporaneous- +switch case ``DID_M`` (= ``DID_1`` of the dynamic companion paper). + +References +---------- +- de Chaisemartin, C. & D'Haultfoeuille, X. (2020). Two-Way Fixed Effects + Estimators with Heterogeneous Treatment Effects. *American Economic + Review*, 110(9), 2964-2996. +- de Chaisemartin, C. & D'Haultfoeuille, X. (2022, revised 2023). + Difference-in-Differences Estimators of Intertemporal Treatment Effects. + NBER Working Paper 29873. +""" + +from dataclasses import dataclass, field +from typing import Any, Dict, List, Optional, Tuple + +import numpy as np +import pandas as pd + +from diff_diff.results import _get_significance_stars + +__all__ = [ + "ChaisemartinDHaultfoeuilleResults", + "DCDHBootstrapResults", +] + + +@dataclass +class DCDHBootstrapResults: + """ + Results from ChaisemartinDHaultfoeuille (dCDH) multiplier bootstrap inference. + + The bootstrap is a library extension beyond the dCDH papers, which + propose only the analytical cohort-recentered plug-in variance from + Web Appendix Section 3.7.3 of the dynamic companion paper. Provided + for consistency with CallawaySantAnna / ImputationDiD / TwoStageDiD. + + Per-target SE / CI / p-value are populated for the three scalar + dCDH estimands implemented in Phase 1: overall (``DID_M``), joiners + (``DID_+``), and leavers (``DID_-``). When a target is not available + in the underlying data (e.g., no leavers), the matching fields are + ``None``. + + **Phase 1 placebo bootstrap is intentionally NOT computed.** The + dynamic companion paper Section 3.7.3 derives the cohort-recentered + analytical variance for ``DID_l`` only, not for the placebo + ``DID_M^pl``. The ``placebo_se`` / ``placebo_ci`` / ``placebo_p_value`` + fields below ALWAYS remain ``None`` in Phase 1, even when + ``n_bootstrap > 0``. Phase 2 will add multiplier-bootstrap support + for the placebo via the dynamic paper's machinery. + + Attributes + ---------- + n_bootstrap : int + Number of bootstrap iterations. + weight_type : str + Type of bootstrap weights: ``"rademacher"``, ``"mammen"``, or + ``"webb"``. + alpha : float + Significance level used for confidence intervals. + overall_se : float + Bootstrap standard error for ``DID_M``. + overall_ci : tuple of float + Bootstrap confidence interval for ``DID_M``. + overall_p_value : float + Bootstrap p-value for ``DID_M``. + joiners_se : float, optional + Bootstrap SE for joiners-only ``DID_+`` (``None`` if no joiners). + joiners_ci : tuple of float, optional + Bootstrap CI for joiners-only ``DID_+``. + joiners_p_value : float, optional + Bootstrap p-value for joiners-only ``DID_+``. + leavers_se : float, optional + Bootstrap SE for leavers-only ``DID_-`` (``None`` if no leavers). + leavers_ci : tuple of float, optional + Bootstrap CI for leavers-only ``DID_-``. + leavers_p_value : float, optional + Bootstrap p-value for leavers-only ``DID_-``. + placebo_se : float, optional + **Always ``None`` in Phase 1** — placebo bootstrap is deferred + to Phase 2 (see class docstring above). + placebo_ci : tuple of float, optional + **Always ``None`` in Phase 1** (see class docstring above). + placebo_p_value : float, optional + **Always ``None`` in Phase 1** (see class docstring above). + bootstrap_distribution : np.ndarray, optional + Full bootstrap distribution of the overall ``DID_M`` estimator + (shape: ``(n_bootstrap,)``). Stored for advanced diagnostics; + suppressed from ``__repr__``. + """ + + n_bootstrap: int + weight_type: str + alpha: float + overall_se: float + overall_ci: Tuple[float, float] + overall_p_value: float + joiners_se: Optional[float] = None + joiners_ci: Optional[Tuple[float, float]] = None + joiners_p_value: Optional[float] = None + leavers_se: Optional[float] = None + leavers_ci: Optional[Tuple[float, float]] = None + leavers_p_value: Optional[float] = None + placebo_se: Optional[float] = None + placebo_ci: Optional[Tuple[float, float]] = None + placebo_p_value: Optional[float] = None + bootstrap_distribution: Optional[np.ndarray] = field(default=None, repr=False) + + +@dataclass +class ChaisemartinDHaultfoeuilleResults: + """ + Results from de Chaisemartin-D'Haultfoeuille (dCDH) Phase 1 estimation. + + Phase 1 ships the contemporaneous-switch estimator ``DID_M`` (= ``DID_1`` + at horizon ``l = 1`` of the dynamic companion paper) plus the joiners- + only / leavers-only views, the single-lag placebo ``DID_M^pl``, and + optionally the TWFE decomposition diagnostic (per-cell weights, + fraction negative, ``sigma_fe``). + + Notes + ----- + The analytical confidence interval is **conservative** under + Assumption 8 (independent groups) of the dynamic companion paper, and + exact only under iid sampling. This is documented as a deliberate + deviation from "default nominal coverage" in the methodology registry. + + For binary treatment in Phase 1, multi-switch groups (i.e., groups + that switch treatment more than once) are dropped before estimation + when ``drop_larger_lower=True`` (the default), matching the R + ``DIDmultiplegtDYN`` reference. The number of dropped groups is + exposed via ``n_groups_dropped_crossers``. + + **Inference-method switch when bootstrap is enabled.** The + ``overall_p_value`` / ``overall_conf_int`` (and joiners/leavers + analogues) fields are populated by *normal-theory* inference from + the cohort-recentered analytical SE when ``n_bootstrap=0`` (the + default). When ``n_bootstrap > 0``, the same fields are populated + by *percentile-based bootstrap inference* from the multiplier + bootstrap distribution computed by ``_compute_dcdh_bootstrap()``. + The t-stat (``overall_t_stat``, etc.) is computed from the SE in + both cases, since percentile bootstrap does not define an + alternative t-stat semantic. ``event_study_effects[1]``, + ``summary()``, ``to_dataframe()``, ``is_significant``, and + ``significance_stars`` all read from these top-level fields and + therefore reflect the bootstrap inference automatically. The + placebo path is unchanged: placebo bootstrap is deferred to Phase + 2, so ``placebo_p_value`` and ``placebo_conf_int`` stay NaN even + when ``n_bootstrap > 0``. See the methodology registry + ``Note (bootstrap inference surface)`` for the full contract and + library precedent. + + Attributes + ---------- + overall_att : float + ``DID_M = DID_1``: the contemporaneous-switch dCDH point estimate. + overall_se : float + Standard error of ``DID_M``. + overall_t_stat : float + overall_p_value : float + overall_conf_int : tuple of float + joiners_att : float + ``DID_+``: the joiners-only contribution. ``NaN`` when + ``joiners_available`` is False. + joiners_se : float + joiners_t_stat : float + joiners_p_value : float + joiners_conf_int : tuple of float + n_joiner_cells : int + Total number of joiner switching ``(g, t)`` cells across all + periods. Each cell counted once. Equals + ``sum_t (#{g : D_{g,t-1}=0, D_{g,t}=1})``. + n_joiner_obs : int + Total raw observation count across joiner cells, summing + ``n_gt`` over the same set of cells. For balanced + one-observation-per-cell panels this equals ``n_joiner_cells``; + for individual-level inputs with multiple observations per + ``(g, t)`` it can be larger. + joiners_available : bool + ``True`` if at least one joiner switching cell exists. + leavers_att : float + ``DID_-``: the leavers-only contribution. ``NaN`` when + ``leavers_available`` is False. + leavers_se : float + leavers_t_stat : float + leavers_p_value : float + leavers_conf_int : tuple of float + n_leaver_cells : int + Total number of leaver switching ``(g, t)`` cells (mirror of + ``n_joiner_cells``). + n_leaver_obs : int + Total raw observation count across leaver cells (mirror of + ``n_joiner_obs``). + leavers_available : bool + placebo_effect : float + ``DID_M^pl``: the single-lag placebo. ``NaN`` when + ``placebo_available`` is False. + placebo_se : float + placebo_t_stat : float + placebo_p_value : float + placebo_conf_int : tuple of float + placebo_available : bool + ``True`` when ``T >= 3`` and at least one qualifying placebo cell + exists. + per_period_effects : dict + Per-period decomposition. Keys are period values; each value is a + dict with the following keys: + + - ``"did_plus_t"`` (float): joiner effect at this period + (``0.0`` if no joiners or A11 violation) + - ``"did_minus_t"`` (float): leaver effect at this period + - ``"n_10_t"`` (int): joiner cell count + - ``"n_01_t"`` (int): leaver cell count + - ``"n_00_t"`` (int): stable-untreated cell count + - ``"n_11_t"`` (int): stable-treated cell count + - ``"did_plus_t_a11_zeroed"`` (bool): True when joiners exist but + no stable-untreated controls (Assumption 11 violation, period + contributes 0 to numerator with non-zero weight in denominator) + - ``"did_minus_t_a11_zeroed"`` (bool): mirror for leavers + twfe_weights : pd.DataFrame, optional + Per-cell TWFE decomposition weights from Theorem 1 of de + Chaisemartin & D'Haultfoeuille (2020). Columns: ``group``, + ``time``, ``weight``. Computed on the **FULL pre-filter cell + sample** passed by the user (the same input the standalone + :func:`twowayfeweights` function uses) — NOT the post-filter + estimation sample described by ``overall_att`` and + ``groups``. When ``fit()`` drops groups via the ragged-panel + or ``drop_larger_lower`` filters, ``results.twfe_*`` and + ``results.overall_att`` describe different samples and a + ``UserWarning`` is emitted; see REGISTRY.md + ``ChaisemartinDHaultfoeuille`` ``Note (TWFE diagnostic + sample contract)`` for the rationale. Only populated when + ``twfe_diagnostic=True``. + twfe_fraction_negative : float, optional + Fraction of treated-cell weights that are negative. ``> 0`` is + the diagnostic for the heterogeneous-treatment-effect bias of + the plain TWFE estimator on the **FULL pre-filter cell sample** + (NOT the post-filter estimation sample). See the + ``twfe_weights`` docstring above for the sample contract. + twfe_sigma_fe : float, optional + Smallest standard deviation of per-cell treatment effects that + could flip the sign of the plain TWFE estimator (Corollary 1 of + the AER 2020 paper). Computed on the **FULL pre-filter cell + sample**. + twfe_beta_fe : float, optional + The plain TWFE coefficient computed on the **FULL pre-filter + cell sample**, for comparison with ``overall_att``. Note that + the two are computed on different samples when ``fit()`` + filters drop groups — see the ``twfe_weights`` docstring above + for the sample contract. + groups : list + Group identifiers in the post-filter sample. + time_periods : list + Time periods in the panel. + n_obs : int + Total observations after filtering. + n_treated_obs : int + Treated observations in the post-filter sample. + n_switcher_cells : int + Number of switching ``(g, t)`` cells across periods. Equals + ``sum_t (n_10_t + n_01_t)`` where each transition cell counts + once regardless of how many original observations fed into it. + This is the ``N_S`` denominator of ``DID_M`` per AER 2020 + Theorem 3 — cell counts, not within-cell observation counts. + n_cohorts : int + Distinct cohorts ``(D_{g,1}, F_g, S_g)`` after filtering. + n_groups_dropped_crossers : int + Number of groups dropped because they were multi-switch (matches + R's ``drop_larger_lower=TRUE`` behavior). ``0`` when + ``drop_larger_lower=False`` or no crossers exist. + n_groups_dropped_singleton_baseline : int + Number of groups whose baseline ``D_{g,1}`` is unique in the + post-drop panel (footnote 15 of the dynamic paper). They are + excluded from the cohort-recentered VARIANCE computation only — + they remain in the point-estimate sample as period-based stable + controls (see REGISTRY.md ``ChaisemartinDHaultfoeuille`` for the + period-vs-cohort deviation that makes this distinction matter). + n_groups_dropped_never_switching : int + Number of groups with ``S_g = 0`` (never switched). **Reported + for backwards compatibility only.** Per the Round 2 full + influence-function fix, never-switching groups are NOT excluded + from the variance: they contribute via their stable-control + roles in the per-period IF formula. The field name retains + "dropped" for API stability but no actual exclusion happens. + alpha : float + Significance level used for confidence intervals. + event_study_effects : dict, optional + In Phase 1 this is populated with a single entry for horizon + ``1``, mirroring ``overall_att``. Keeping the field shape stable + avoids API churn when Phase 2 adds entries for ``l = 2, ..., L``. + normalized_effects : dict, optional + Phase 2 placeholder (``DID^n_l``). Always ``None`` in Phase 1. + cost_benefit_delta : dict, optional + Phase 2 placeholder (cost-benefit aggregate ``delta``). Always + ``None`` in Phase 1. + sup_t_bands : dict, optional + Phase 2 placeholder (sup-t simultaneous confidence bands). + covariate_residuals : pd.DataFrame, optional + Phase 3 placeholder (``DID^X`` residuals). + linear_trends_effects : dict, optional + Phase 3 placeholder (``DID^{fd}`` group-specific linear trends). + honest_did_results : Any, optional + Phase 3 placeholder (HonestDiD integration on placebos). + survey_metadata : Any, optional + Always ``None`` in Phase 1 — survey integration is deferred to a + separate effort after all phases ship. + bootstrap_results : DCDHBootstrapResults, optional + Bootstrap inference results when ``n_bootstrap > 0``. + """ + + # --- Core: DID_M aggregate --- + overall_att: float + overall_se: float + overall_t_stat: float + overall_p_value: float + overall_conf_int: Tuple[float, float] + + # --- Joiners-only view (DID_+) --- + joiners_att: float + joiners_se: float + joiners_t_stat: float + joiners_p_value: float + joiners_conf_int: Tuple[float, float] + n_joiner_cells: int + n_joiner_obs: int + joiners_available: bool + + # --- Leavers-only view (DID_-) --- + leavers_att: float + leavers_se: float + leavers_t_stat: float + leavers_p_value: float + leavers_conf_int: Tuple[float, float] + n_leaver_cells: int + n_leaver_obs: int + leavers_available: bool + + # --- Placebo (DID_M^pl) --- + placebo_effect: float + placebo_se: float + placebo_t_stat: float + placebo_p_value: float + placebo_conf_int: Tuple[float, float] + placebo_available: bool + + # --- Per-period decomposition --- + per_period_effects: Dict[Any, Dict[str, Any]] + + # --- Metadata --- + groups: List[Any] + time_periods: List[Any] + n_obs: int + n_treated_obs: int + n_switcher_cells: int + n_cohorts: int + n_groups_dropped_crossers: int + n_groups_dropped_singleton_baseline: int + n_groups_dropped_never_switching: int + + # --- Phase 1 event-study placeholder (populated with l=1 entry) --- + # Stable shape across phases. In Phase 1, populated with a single + # entry {1: {effect, se, t_stat, p_value, conf_int, n_obs}} mirroring + # overall_att. Phase 2 extends with entries for l = 2, ..., L_max. + event_study_effects: Optional[Dict[int, Dict[str, Any]]] = None + + # --- TWFE decomposition diagnostic (Theorem 1 of AER 2020) --- + twfe_weights: Optional[pd.DataFrame] = field(default=None, repr=False) + twfe_fraction_negative: Optional[float] = None + twfe_sigma_fe: Optional[float] = None + twfe_beta_fe: Optional[float] = None + + alpha: float = 0.05 + + # --- Forward-compat placeholders (always None in Phase 1) --- + normalized_effects: Optional[Dict[int, Dict[str, Any]]] = field(default=None, repr=False) + cost_benefit_delta: Optional[Dict[str, Any]] = field(default=None, repr=False) + sup_t_bands: Optional[Dict[str, Any]] = field(default=None, repr=False) + covariate_residuals: Optional[pd.DataFrame] = field(default=None, repr=False) + linear_trends_effects: Optional[Dict[int, Dict[str, Any]]] = field(default=None, repr=False) + honest_did_results: Optional[Any] = field(default=None, repr=False) + + # --- Repr-suppressed metadata --- + survey_metadata: Optional[Any] = field(default=None, repr=False) + bootstrap_results: Optional[DCDHBootstrapResults] = field(default=None, repr=False) + _estimator_ref: Optional[Any] = field(default=None, repr=False) + + # ------------------------------------------------------------------ + # Repr / properties + # ------------------------------------------------------------------ + + def __repr__(self) -> str: + """Concise string representation.""" + sig = _get_significance_stars(self.overall_p_value) + return ( + f"ChaisemartinDHaultfoeuilleResults(" + f"DID_M={self.overall_att:.4f}{sig}, " + f"SE={self.overall_se:.4f}, " + f"n_groups={len(self.groups)}, " + f"n_switcher_cells={self.n_switcher_cells})" + ) + + @property + def coef_var(self) -> float: + """SE / |DID_M|; NaN when DID_M is 0 or SE non-finite.""" + if not (np.isfinite(self.overall_se) and self.overall_se >= 0): + return np.nan + if not np.isfinite(self.overall_att) or self.overall_att == 0: + return np.nan + return self.overall_se / abs(self.overall_att) + + @property + def is_significant(self) -> bool: + """True iff overall ``DID_M`` p-value is below ``alpha``.""" + return bool(self.overall_p_value < self.alpha) + + @property + def significance_stars(self) -> str: + """Significance stars for the overall ``DID_M``.""" + return _get_significance_stars(self.overall_p_value) + + # ------------------------------------------------------------------ + # Summary + # ------------------------------------------------------------------ + + def summary(self, alpha: Optional[float] = None) -> str: + """ + Generate a formatted summary of dCDH estimation results. + + Parameters + ---------- + alpha : float, optional + Significance level for the confidence interval header. Defaults + to ``self.alpha``. + + Returns + ------- + str + Formatted multi-block summary including overall ``DID_M``, + joiners-only / leavers-only views, the placebo, the TWFE + decomposition diagnostic, and a footer of significance codes. + """ + alpha = alpha or self.alpha + conf_level = int((1 - alpha) * 100) + width = 85 + sep = "=" * width + thin = "-" * width + header_row = ( + f"{'Parameter':<15} {'Estimate':>12} {'Std. Err.':>12} " + f"{'t-stat':>10} {'P>|t|':>10} {'Sig.':>6}" + ) + + lines = [ + sep, + "de Chaisemartin-D'Haultfoeuille (dCDH) Estimator Results".center(width), + sep, + "", + f"{'Total observations:':<35} {self.n_obs:>10}", + f"{'Treated observations:':<35} {self.n_treated_obs:>10}", + f"{'Switcher cells (N_S):':<35} {self.n_switcher_cells:>10}", + f"{'Groups (post-filter):':<35} {len(self.groups):>10}", + f"{'Cohorts:':<35} {self.n_cohorts:>10}", + f"{'Time periods:':<35} {len(self.time_periods):>10}", + "", + ] + + # Filter counts (only show if any drops/exclusions happened). + # After Round 2, never-switching groups participate in the variance + # via stable-control roles and are NOT dropped — their count is + # reported here for backwards compatibility only. + if ( + self.n_groups_dropped_crossers + + self.n_groups_dropped_singleton_baseline + + self.n_groups_dropped_never_switching + > 0 + ): + lines.extend( + [ + "Group filter / metadata counts:", + f"{' Multi-switch (dropped):':<42} " f"{self.n_groups_dropped_crossers:>10}", + f"{' Singleton baseline (variance only):':<42} " + f"{self.n_groups_dropped_singleton_baseline:>10}", + f"{' Never-switching (reported, not dropped):':<42} " + f"{self.n_groups_dropped_never_switching:>10}", + "", + ] + ) + + # --- Overall DID_M --- + lines.extend( + [ + thin, + "DID_M (Contemporaneous-Switch ATT)".center(width), + thin, + header_row, + thin, + _format_inference_row( + "DID_M", + self.overall_att, + self.overall_se, + self.overall_t_stat, + self.overall_p_value, + ), + thin, + "", + f"{conf_level}% Confidence Interval: " + f"[{_fmt_float(self.overall_conf_int[0])}, " + f"{_fmt_float(self.overall_conf_int[1])}]", + ] + ) + + cv = self.coef_var + if np.isfinite(cv): + lines.append(f"{'CV (SE/|DID_M|):':<25} {cv:>10.4f}") + + lines.append("") + if self.bootstrap_results is not None: + lines.append("Note: p-value and CI are multiplier-bootstrap percentile inference") + lines.append( + f" ({self.bootstrap_results.n_bootstrap} iterations, " + f"{self.bootstrap_results.weight_type} weights)." + ) + else: + lines.append( + "Note: dCDH analytical CI is conservative under Assumption 8" + " (independent groups);" + ) + lines.append(" exact under iid sampling.") + lines.append("") + + # --- Joiners and leavers --- + lines.extend( + [ + thin, + "Decomposition: Joiners (DID_+) and Leavers (DID_-)".center(width), + thin, + header_row, + thin, + ] + ) + + if self.joiners_available: + lines.append( + _format_inference_row( + "DID_+", + self.joiners_att, + self.joiners_se, + self.joiners_t_stat, + self.joiners_p_value, + ) + ) + lines.append( + f" ({self.n_joiner_cells} joiner cells, " f"{self.n_joiner_obs} observations)" + ) + else: + lines.append( + f"{'DID_+':<15} {'(no joiners)':>12} " f"{'':>12} {'':>10} {'':>10} {'':>6}" + ) + + if self.leavers_available: + lines.append( + _format_inference_row( + "DID_-", + self.leavers_att, + self.leavers_se, + self.leavers_t_stat, + self.leavers_p_value, + ) + ) + lines.append( + f" ({self.n_leaver_cells} leaver cells, " f"{self.n_leaver_obs} observations)" + ) + else: + lines.append( + f"{'DID_-':<15} {'(no leavers)':>12} " f"{'':>12} {'':>10} {'':>10} {'':>6}" + ) + + lines.extend([thin, ""]) + + # --- Placebo --- + if self.placebo_available: + lines.extend( + [ + thin, + "Single-Lag Placebo (DID_M^pl)".center(width), + thin, + header_row, + thin, + _format_inference_row( + "DID_M^pl", + self.placebo_effect, + self.placebo_se, + self.placebo_t_stat, + self.placebo_p_value, + ), + thin, + "Under parallel trends, the placebo should be ~0.", + "", + ] + ) + else: + lines.extend( + [ + thin, + "Placebo not available (T < 3 or no qualifying cells)".center(width), + thin, + "", + ] + ) + + # --- TWFE diagnostic --- + if self.twfe_beta_fe is not None: + lines.extend( + [ + thin, + "TWFE Decomposition Diagnostic (Theorem 1, AER 2020)".center(width), + thin, + f"{'Plain TWFE coefficient:':<35} {_fmt_float(self.twfe_beta_fe):>10}", + ] + ) + if self.twfe_fraction_negative is not None: + lines.append( + f"{'Fraction of negative weights:':<35} " + f"{self.twfe_fraction_negative:>10.4f}" + ) + if self.twfe_sigma_fe is not None and np.isfinite(self.twfe_sigma_fe): + lines.append( + f"{'Sigma_fe (sign-flip threshold):':<35} " f"{self.twfe_sigma_fe:>10.4f}" + ) + lines.extend( + [ + "", + "A positive fraction of negative weights signals that the plain", + "TWFE coefficient may have the wrong sign under heterogeneous", + "treatment effects. Sigma_fe is the smallest cell-level effect", + "standard deviation that could flip the sign of TWFE.", + thin, + "", + ] + ) + + lines.extend( + [ + "Signif. codes: '***' 0.001, '**' 0.01, '*' 0.05, '.' 0.1", + sep, + ] + ) + + return "\n".join(lines) + + def print_summary(self, alpha: Optional[float] = None) -> None: + """Print the formatted summary to stdout.""" + print(self.summary(alpha)) + + # ------------------------------------------------------------------ + # to_dataframe + # ------------------------------------------------------------------ + + def to_dataframe(self, level: str = "overall") -> pd.DataFrame: + """ + Convert results to a DataFrame at the requested level of aggregation. + + Parameters + ---------- + level : str, default="overall" + One of: + + - ``"overall"``: single-row table with the overall ``DID_M`` + point estimate, SE, t-stat, p-value, CI bounds. + - ``"joiners_leavers"``: three rows for ``DID_M``, ``DID_+``, + and ``DID_-``. + - ``"per_period"``: one row per time period with + ``did_plus_t``, ``did_minus_t``, switching cell counts, and + the A11-zeroed flags. + - ``"twfe_weights"``: per-(group, time) TWFE decomposition + weights table. Only available when ``twfe_diagnostic=True`` + was passed to ``fit()``. + + Returns + ------- + pd.DataFrame + """ + if level == "overall": + return pd.DataFrame( + [ + { + "estimand": "DID_M", + "effect": self.overall_att, + "se": self.overall_se, + "t_stat": self.overall_t_stat, + "p_value": self.overall_p_value, + "conf_int_lower": self.overall_conf_int[0], + "conf_int_upper": self.overall_conf_int[1], + } + ] + ) + + elif level == "joiners_leavers": + # Two separate count columns so each has consistent units + # across all rows: + # n_cells: total switching cells (each (g, t) cell counted once) + # n_obs: actual observation count summed over the same cells + # (equals n_cells on balanced 1-obs-per-cell panels; + # larger on individual-level inputs with multiple + # observations per cell). + # For the DID_M row, both quantities use the overall switching + # cell set: n_cells = sum of joiner + leaver cells, and n_obs + # is the same sum of raw observation counts. + rows = [ + { + "estimand": "DID_M", + "effect": self.overall_att, + "se": self.overall_se, + "t_stat": self.overall_t_stat, + "p_value": self.overall_p_value, + "conf_int_lower": self.overall_conf_int[0], + "conf_int_upper": self.overall_conf_int[1], + "n_cells": self.n_switcher_cells, + "n_obs": self.n_joiner_obs + self.n_leaver_obs, + "available": True, + }, + { + "estimand": "DID_+", + "effect": self.joiners_att, + "se": self.joiners_se, + "t_stat": self.joiners_t_stat, + "p_value": self.joiners_p_value, + "conf_int_lower": self.joiners_conf_int[0], + "conf_int_upper": self.joiners_conf_int[1], + "n_cells": self.n_joiner_cells, + "n_obs": self.n_joiner_obs, + "available": self.joiners_available, + }, + { + "estimand": "DID_-", + "effect": self.leavers_att, + "se": self.leavers_se, + "t_stat": self.leavers_t_stat, + "p_value": self.leavers_p_value, + "conf_int_lower": self.leavers_conf_int[0], + "conf_int_upper": self.leavers_conf_int[1], + "n_cells": self.n_leaver_cells, + "n_obs": self.n_leaver_obs, + "available": self.leavers_available, + }, + ] + return pd.DataFrame(rows) + + elif level == "per_period": + if not self.per_period_effects: + # Empty per-period table — return DataFrame with the + # canonical column order so downstream code can rely on it. + return pd.DataFrame( + { + "period": pd.Series(dtype="int64"), + "did_plus_t": pd.Series(dtype="float64"), + "did_minus_t": pd.Series(dtype="float64"), + "n_10_t": pd.Series(dtype="int64"), + "n_01_t": pd.Series(dtype="int64"), + "n_00_t": pd.Series(dtype="int64"), + "n_11_t": pd.Series(dtype="int64"), + "did_plus_t_a11_zeroed": pd.Series(dtype="bool"), + "did_minus_t_a11_zeroed": pd.Series(dtype="bool"), + } + ) + rows = [] + for t in sorted(self.per_period_effects.keys()): + cell = self.per_period_effects[t] + rows.append({"period": t, **cell}) + return pd.DataFrame(rows) + + elif level == "twfe_weights": + if self.twfe_weights is None: + raise ValueError( + "TWFE decomposition weights not computed. Pass " + "twfe_diagnostic=True (the default) to ChaisemartinDHaultfoeuille()." + ) + return self.twfe_weights.copy() + + else: + raise ValueError( + f"Unknown level: {level!r}. Use 'overall', 'joiners_leavers', " + f"'per_period', or 'twfe_weights'." + ) + + +# ============================================================================= +# Internal formatting helpers +# ============================================================================= + + +def _fmt_float(x: float) -> str: + """Format a float; render NaN/Inf as the string 'NaN'/'Inf'.""" + if not np.isfinite(x): + return "NaN" if np.isnan(x) else ("Inf" if x > 0 else "-Inf") + return f"{x:.4f}" + + +def _format_inference_row( + label: str, + effect: float, + se: float, + t_stat: float, + p_value: float, +) -> str: + """Format a single inference row for the summary table.""" + e_str = f"{_fmt_float(effect):>12}" + s_str = f"{_fmt_float(se):>12}" + t_str = f"{t_stat:>10.3f}" if np.isfinite(t_stat) else f"{'NaN':>10}" + p_str = f"{p_value:>10.4f}" if np.isfinite(p_value) else f"{'NaN':>10}" + sig = _get_significance_stars(p_value) if np.isfinite(p_value) else "" + return f"{label:<15} {e_str} {s_str} {t_str} {p_str} {sig:>6}" diff --git a/diff_diff/prep.py b/diff_diff/prep.py index c5bdbdb28..26f18ab48 100644 --- a/diff_diff/prep.py +++ b/diff_diff/prep.py @@ -22,6 +22,7 @@ generate_event_study_data, generate_factor_data, generate_panel_data, + generate_reversible_did_data, generate_staggered_data, generate_staggered_ddd_data, generate_survey_did_data, diff --git a/diff_diff/prep_dgp.py b/diff_diff/prep_dgp.py index 546a5a25b..79919b62e 100644 --- a/diff_diff/prep_dgp.py +++ b/diff_diff/prep_dgp.py @@ -1323,9 +1323,7 @@ def generate_survey_did_data( if psu_per_stratum < 1: raise ValueError(f"psu_per_stratum must be positive, got {psu_per_stratum}") if not 0.0 <= never_treated_frac <= 1.0: - raise ValueError( - f"never_treated_frac must be between 0 and 1, got {never_treated_frac}" - ) + raise ValueError(f"never_treated_frac must be between 0 and 1, got {never_treated_frac}") if fpc_per_stratum < psu_per_stratum: raise ValueError( f"fpc_per_stratum ({fpc_per_stratum}) must be >= psu_per_stratum " @@ -1351,9 +1349,7 @@ def generate_survey_did_data( raise ValueError("cohort_periods must be a non-empty list of integers") for cp in cohort_periods: if isinstance(cp, bool) or not isinstance(cp, (int, np.integer)): - raise ValueError( - f"cohort_periods must contain integers, got {cp!r}" - ) + raise ValueError(f"cohort_periods must contain integers, got {cp!r}") if cp < 2 or cp > n_periods: raise ValueError( f"Cohort period {cp} must be between 2 and {n_periods} " @@ -1362,15 +1358,12 @@ def generate_survey_did_data( if not np.isfinite(psu_period_factor) or psu_period_factor < 0: raise ValueError( - f"psu_period_factor must be finite and non-negative, " - f"got {psu_period_factor}" + f"psu_period_factor must be finite and non-negative, " f"got {psu_period_factor}" ) valid_wv = ("none", "moderate", "high") if weight_variation not in valid_wv: - raise ValueError( - f"weight_variation must be one of {valid_wv}, got {weight_variation!r}" - ) + raise ValueError(f"weight_variation must be one of {valid_wv}, got {weight_variation!r}") # --- Validate research-grade DGP parameters --- if icc is not None: @@ -1384,9 +1377,7 @@ def generate_survey_did_data( if weight_cv is not None: if not np.isfinite(weight_cv) or weight_cv <= 0: - raise ValueError( - f"weight_cv must be finite and positive, got {weight_cv}" - ) + raise ValueError(f"weight_cv must be finite and positive, got {weight_cv}") if weight_variation != "moderate": raise ValueError( "Cannot specify both weight_cv and a non-default " @@ -1397,43 +1388,31 @@ def generate_survey_did_data( strata_sizes = list(strata_sizes) for ss in strata_sizes: if isinstance(ss, bool) or not isinstance(ss, (int, np.integer)): - raise ValueError( - f"strata_sizes must contain integers, got {ss!r}" - ) + raise ValueError(f"strata_sizes must contain integers, got {ss!r}") if len(strata_sizes) != n_strata: raise ValueError( - f"strata_sizes must have length n_strata={n_strata}, " - f"got {len(strata_sizes)}" + f"strata_sizes must have length n_strata={n_strata}, " f"got {len(strata_sizes)}" ) if any(s < 1 for s in strata_sizes): raise ValueError("All strata_sizes must be >= 1") if sum(strata_sizes) != n_units: raise ValueError( - f"strata_sizes must sum to n_units={n_units}, " - f"got {sum(strata_sizes)}" + f"strata_sizes must sum to n_units={n_units}, " f"got {sum(strata_sizes)}" ) # --- Validate and resolve covariate coefficients --- if covariate_effects is not None: covariate_effects = tuple(covariate_effects) if len(covariate_effects) != 2: - raise ValueError( - f"covariate_effects must have length 2, got {len(covariate_effects)}" - ) + raise ValueError(f"covariate_effects must have length 2, got {len(covariate_effects)}") if not all(np.isfinite(c) for c in covariate_effects): - raise ValueError( - f"covariate_effects must be finite, got {covariate_effects}" - ) + raise ValueError(f"covariate_effects must be finite, got {covariate_effects}") _beta1, _beta2 = covariate_effects if covariate_effects is not None else (0.5, 0.3) if not np.isfinite(te_covariate_interaction): - raise ValueError( - f"te_covariate_interaction must be finite, got {te_covariate_interaction}" - ) + raise ValueError(f"te_covariate_interaction must be finite, got {te_covariate_interaction}") if te_covariate_interaction != 0.0 and not add_covariates: - raise ValueError( - "te_covariate_interaction requires add_covariates=True" - ) + raise ValueError("te_covariate_interaction requires add_covariates=True") # --- ICC -> psu_re_sd resolution --- if icc is not None: @@ -1446,10 +1425,7 @@ def generate_survey_did_data( "icc requires non-zero non-PSU variance " "(unit_fe_sd, noise_sd, or add_covariates must contribute variance)" ) - psu_re_sd = np.sqrt( - icc * non_psu_var - / ((1 - icc) * (1 + psu_period_factor**2)) - ) + psu_re_sd = np.sqrt(icc * non_psu_var / ((1 - icc) * (1 + psu_period_factor**2))) # --- Survey structure: assign units to strata and PSUs --- n_psu_total = n_strata * psu_per_stratum @@ -1459,10 +1435,7 @@ def generate_survey_did_data( else: units_per_stratum = n_units // n_strata remainder = n_units % n_strata - stratum_n = [ - units_per_stratum + (1 if s < remainder else 0) - for s in range(n_strata) - ] + stratum_n = [units_per_stratum + (1 if s < remainder else 0) for s in range(n_strata)] unit_stratum = np.empty(n_units, dtype=int) unit_psu = np.empty(n_units, dtype=int) @@ -1478,7 +1451,7 @@ def generate_survey_did_data( # Sampling weights if weight_cv is not None: sigma_ln = np.sqrt(np.log(1 + weight_cv**2)) - raw_w = rng.lognormal(-sigma_ln**2 / 2, sigma_ln, size=n_units) + raw_w = rng.lognormal(-(sigma_ln**2) / 2, sigma_ln, size=n_units) unit_weight = raw_w / raw_w.mean() else: # Stratum-based weights (inverse selection probability) @@ -1495,20 +1468,13 @@ def generate_survey_did_data( unit_cohort = np.zeros(n_units, dtype=int) ci = n_never for i, g in enumerate(cohort_periods): - n_g = ( - n_per_cohort - if i < len(cohort_periods) - 1 - else n_treated_total - ci + n_never - ) + n_g = n_per_cohort if i < len(cohort_periods) - 1 else n_treated_total - ci + n_never unit_cohort[ci : ci + n_g] = g ci += n_g # --- JK1 early guard (configured count; populated count checked after build) --- if include_replicate_weights and n_psu_total < 2: - raise ValueError( - "JK1 replicate weights require at least 2 PSUs, " - f"got {n_psu_total}." - ) + raise ValueError("JK1 replicate weights require at least 2 PSUs, " f"got {n_psu_total}.") # --- Random effects --- psu_re = rng.normal(0, psu_re_sd, size=n_psu_total) @@ -1518,19 +1484,12 @@ def generate_survey_did_data( # cluster-robust / survey SE would be *smaller* than naive OLS SE. # Controlled by psu_period_factor (default 0.5); higher values # increase time-varying clustering and inflate design-based SEs. - psu_period_re = rng.normal( - 0, psu_re_sd * psu_period_factor, size=(n_psu_total, n_periods) - ) + psu_period_re = rng.normal(0, psu_re_sd * psu_period_factor, size=(n_psu_total, n_periods)) # --- Informative sampling (panel path): pre-draw FEs, rank-pair weights --- if informative_sampling and panel: _panel_unit_fe = rng.normal(0, unit_fe_sd, size=n_units) - y0_period1 = ( - _panel_unit_fe - + psu_re[unit_psu] - + psu_period_re[unit_psu, 0] - + 0.5 - ) + y0_period1 = _panel_unit_fe + psu_re[unit_psu] + psu_period_re[unit_psu, 0] + 0.5 if add_covariates: _panel_x1 = rng.normal(0, 1, size=n_units) _panel_x2 = rng.choice([0, 1], size=n_units) @@ -1574,12 +1533,7 @@ def generate_survey_did_data( x1 = rng.normal(0, 1, size=n_units) x2 = rng.choice([0, 1], size=n_units) unit_weight = _base_weight.copy() # type: ignore[possibly-undefined] - y0_t = ( - unit_fe - + psu_re[unit_psu] - + psu_period_re[unit_psu, t - 1] - + 0.5 * t - ) + y0_t = unit_fe + psu_re[unit_psu] + psu_period_re[unit_psu, t - 1] + 0.5 * t if add_covariates: y0_t = y0_t + _beta1 * x1 + _beta2 * x2 _rank_pair_weights(unit_weight, unit_stratum, y0_t, n_strata) @@ -1677,13 +1631,9 @@ def generate_survey_did_data( population_att = float("nan") if te_by_stratum is not None: - stratum_effects = { - int(s): float(te_by_stratum[s]) for s in range(n_strata) - } + stratum_effects = {int(s): float(te_by_stratum[s]) for s in range(n_strata)} else: - stratum_effects = { - int(s): float(treatment_effect) for s in range(n_strata) - } + stratum_effects = {int(s): float(treatment_effect) for s in range(n_strata)} # Kish DEFF from weight variation w_all = df.groupby("unit")["weight"].first().values @@ -1716,3 +1666,343 @@ def generate_survey_did_data( } return df + + +# ============================================================================= +# Reversible-treatment data generator (dCDH / ChaisemartinDHaultfoeuille) +# ============================================================================= + + +def _generate_reversible_treatment_matrix( + pattern: str, + n_groups: int, + n_periods: int, + p_switch: float, + initial_treat_frac: float, + cycle_length: int, + rng: np.random.Generator, +) -> np.ndarray: + """ + Internal helper for ``generate_reversible_did_data``. + + Returns an ``(n_groups, n_periods)`` int array of binary treatment values. + """ + D = np.zeros((n_groups, n_periods), dtype=int) + + if pattern == "single_switch": + # Mix of joiners and leavers based on initial_treat_frac. + # Each group switches exactly once at a uniform-random time in [1, n_periods - 1]. + initial_treated = rng.random(n_groups) < initial_treat_frac + switch_times = rng.integers(1, n_periods, size=n_groups) + for g in range(n_groups): + if initial_treated[g]: + # Starts treated, switches to untreated at switch_times[g] + D[g, : switch_times[g]] = 1 + D[g, switch_times[g] :] = 0 + else: + # Starts untreated, switches to treated at switch_times[g] + D[g, : switch_times[g]] = 0 + D[g, switch_times[g] :] = 1 + + elif pattern == "joiners_only": + # All groups start untreated, each switches to treated once at random time + switch_times = rng.integers(1, n_periods, size=n_groups) + for g in range(n_groups): + D[g, switch_times[g] :] = 1 + + elif pattern == "leavers_only": + # All groups start treated, each switches to untreated once at random time + switch_times = rng.integers(1, n_periods, size=n_groups) + for g in range(n_groups): + D[g, : switch_times[g]] = 1 + + elif pattern == "mixed_single_switch": + # Deterministic: first half are joiners, second half are leavers. + # Each group switches exactly once at a uniform-random time. + switch_times = rng.integers(1, n_periods, size=n_groups) + n_joiners = n_groups // 2 + for g in range(n_groups): + if g < n_joiners: + D[g, switch_times[g] :] = 1 # Joiner + else: + D[g, : switch_times[g]] = 1 # Leaver + + elif pattern == "random": + # Initial state random, then flip with probability p_switch each subsequent period. + # Often produces multi-switch groups for n_periods >= 4 and p_switch > 0. + D[:, 0] = (rng.random(n_groups) < initial_treat_frac).astype(int) + for t in range(1, n_periods): + flips = rng.random(n_groups) < p_switch + D[:, t] = np.where(flips, 1 - D[:, t - 1], D[:, t - 1]) + + elif pattern == "cycles": + # Deterministic on/off cycles of length cycle_length. + # Half the groups start in the "0" phase, half start in the "1" phase. + # All groups are multi-switch when n_periods > 2 * cycle_length. + for t in range(n_periods): + phase = (t // cycle_length) % 2 + n_first_half = n_groups // 2 + D[:n_first_half, t] = phase + D[n_first_half:, t] = 1 - phase + + elif pattern == "marketing": + # Seasonal "2 on, 1 off" pattern, identical for all groups. + # All groups are multi-switch when n_periods >= 4. + for t in range(n_periods): + phase_in_cycle = t % 3 + on = phase_in_cycle != 2 + D[:, t] = int(on) + + return D + + +def generate_reversible_did_data( + n_groups: int = 50, + n_periods: int = 6, + pattern: str = "single_switch", + p_switch: float = 0.2, + initial_treat_frac: float = 0.3, + cycle_length: int = 2, + treatment_effect: float = 2.0, + heterogeneous_effects: bool = False, + effect_sd: float = 0.5, + group_fe_sd: float = 2.0, + time_trend: float = 0.1, + noise_sd: float = 0.5, + seed: Optional[int] = None, +) -> pd.DataFrame: + """ + Generate synthetic panel data with reversible (non-absorbing) treatment. + + Treatment can switch on and off over time, supporting designs where the + canonical staggered-adoption assumption (once treated, always treated) + does not hold. This is the only generator in the library that produces + reversible-treatment data; intended for the + :class:`~diff_diff.ChaisemartinDHaultfoeuille` (dCDH) estimator. + + Seven patterns are supported. Four of them are guaranteed to keep every + group as a "single-switch" group (each group switches treatment status + at most once), so the dCDH ``drop_larger_lower=True`` filter is a no-op. + The other three deliberately produce multi-switch groups for stress- + testing the drop logic. + + Parameters + ---------- + n_groups : int, default=50 + Number of groups in the panel. + n_periods : int, default=6 + Number of time periods. Must be at least 2. + pattern : str, default="single_switch" + Treatment pattern. One of: + + - ``"single_switch"`` (default, single-switch): each group switches + exactly once at a uniform-random time. Mix of joiners and leavers + determined by ``initial_treat_frac``. + - ``"joiners_only"`` (single-switch): all groups start untreated and + each switches to treated once. Pure staggered adoption. + - ``"leavers_only"`` (single-switch): mirror of ``joiners_only`` — + all groups start treated and each switches to untreated once. + - ``"mixed_single_switch"`` (single-switch): deterministic 50/50 mix + of joiners and leavers, each with exactly one switch. Useful for + parity tests where you want a guaranteed split independent of seed. + - ``"random"`` (often multi-switch): each ``(g, t >= 1)`` flips + treatment from the previous period with probability ``p_switch``. + Initial state drawn from ``Bernoulli(initial_treat_frac)``. With + ``n_periods >= 4`` and ``p_switch > 0``, many groups will switch + more than once and will be dropped under + ``drop_larger_lower=True``. Useful for stress-testing the drop + filter. + - ``"cycles"`` (always multi-switch): deterministic on/off cycles of + length ``cycle_length``. Half the groups start in the "0" phase + and half in the "1" phase, so the panel always contains both + joiner and leaver transitions. Every group is multi-switch when + ``n_periods > 2 * cycle_length``. + - ``"marketing"`` (always multi-switch): seasonal "2 on, 1 off" + pattern starting in the on phase, identical across groups. Mimics + a marketing campaign with periodic breaks. + p_switch : float, default=0.2 + Per-period flip probability. Only used when ``pattern="random"``. + initial_treat_frac : float, default=0.3 + Fraction of groups starting in the treated state at period 0. Only + used by ``"single_switch"`` and ``"random"``. + cycle_length : int, default=2 + Length of each on/off phase. Only used when ``pattern="cycles"``. + treatment_effect : float, default=2.0 + Average treatment effect on treated cells. With + ``heterogeneous_effects=False``, every treated cell has exactly this + effect. With ``True``, this is the mean of a Normal distribution. + heterogeneous_effects : bool, default=False + If True, per-cell true effects are drawn independently from + ``Normal(treatment_effect, effect_sd)``. + effect_sd : float, default=0.5 + Standard deviation of per-cell effects when + ``heterogeneous_effects=True``. + group_fe_sd : float, default=2.0 + Standard deviation of group fixed effects. + time_trend : float, default=0.1 + Linear time trend coefficient. + noise_sd : float, default=0.5 + Standard deviation of idiosyncratic noise. + seed : int, optional + Random seed for reproducibility. + + Returns + ------- + pd.DataFrame + Synthetic balanced panel with one row per ``(group, period)`` cell + and the following columns: + + - ``group`` (int): group identifier in ``[0, n_groups)`` + - ``period`` (int): time period in ``[0, n_periods)`` + - ``treatment`` (int): per-cell binary treatment (0 or 1) + - ``outcome`` (float): outcome variable + - ``true_effect`` (float): per-cell true treatment effect (0 if + untreated) + - ``d_lag`` (float): previous-period treatment, NaN at period 0 + - ``switcher_type`` (object): one of ``"initial"`` (period 0), + ``"joiner"`` (``d_lag=0, treatment=1``), ``"leaver"`` + (``d_lag=1, treatment=0``), ``"stable_0"`` + (``d_lag=0, treatment=0``), or ``"stable_1"`` + (``d_lag=1, treatment=1``) + + Notes + ----- + The default pattern is ``"single_switch"`` so the generator's happy path + produces data that the dCDH estimator can use directly without dropping + groups. The ``"random"``, ``"cycles"``, and ``"marketing"`` patterns are + primarily for stress-testing the ``drop_larger_lower=True`` filter and + will produce data where many or all groups are filtered out before + estimation. + + The default ``pattern="single_switch"`` is **A5-safe by construction**: + every group has at most one transition, so no group can be a "crosser" + that switches in and back out. The dCDH estimator's + ``drop_larger_lower=True`` filter (matching R ``DIDmultiplegtDYN``) is + a no-op on this pattern. Other patterns (``random``, ``cycles``, + ``marketing``) ARE allowed to violate A5 and are useful primarily for + stress-testing the multi-switch drop filter — passing them through the + estimator with ``drop_larger_lower=True`` should drop a non-zero count + of crosser groups, which is the intended check. The cohort-recentered + variance formula in Web Appendix Section 3.7.3 of the dynamic + companion paper is derived under A5, which is why the drop filter is + on by default. + + Examples + -------- + Default single-switch panel (mix of joiners and leavers, all groups + survive ``drop_larger_lower=True``): + + >>> data = generate_reversible_did_data(n_groups=20, n_periods=6, seed=42) + >>> sorted(data.columns.tolist()) + ['d_lag', 'group', 'outcome', 'period', 'switcher_type', 'treatment', 'true_effect'] + >>> set(data['switcher_type']).issubset( + ... {'initial', 'joiner', 'leaver', 'stable_0', 'stable_1'} + ... ) + True + + Joiners-only (pure staggered adoption): + + >>> data = generate_reversible_did_data( + ... n_groups=20, pattern="joiners_only", seed=1 + ... ) + >>> set(data.query("period == 0")['treatment'].unique()) == {0} + True + + Leavers-only: + + >>> data = generate_reversible_did_data( + ... n_groups=20, pattern="leavers_only", seed=2 + ... ) + >>> set(data.query("period == 0")['treatment'].unique()) == {1} + True + """ + # --- Parameter validation --- + valid_patterns = { + "single_switch", + "joiners_only", + "leavers_only", + "mixed_single_switch", + "random", + "cycles", + "marketing", + } + if pattern not in valid_patterns: + raise ValueError(f"pattern must be one of {sorted(valid_patterns)}, got {pattern!r}") + if n_groups < 1: + raise ValueError(f"n_groups must be positive, got {n_groups}") + if n_periods < 2: + raise ValueError(f"n_periods must be at least 2, got {n_periods}") + if not 0.0 <= initial_treat_frac <= 1.0: + raise ValueError(f"initial_treat_frac must be in [0, 1], got {initial_treat_frac}") + if not 0.0 <= p_switch <= 1.0: + raise ValueError(f"p_switch must be in [0, 1], got {p_switch}") + if cycle_length < 1: + raise ValueError(f"cycle_length must be positive, got {cycle_length}") + if effect_sd < 0: + raise ValueError(f"effect_sd must be non-negative, got {effect_sd}") + if group_fe_sd < 0: + raise ValueError(f"group_fe_sd must be non-negative, got {group_fe_sd}") + if noise_sd < 0: + raise ValueError(f"noise_sd must be non-negative, got {noise_sd}") + + rng = np.random.default_rng(seed) + + # --- Generate the (n_groups, n_periods) treatment matrix --- + D = _generate_reversible_treatment_matrix( + pattern=pattern, + n_groups=n_groups, + n_periods=n_periods, + p_switch=p_switch, + initial_treat_frac=initial_treat_frac, + cycle_length=cycle_length, + rng=rng, + ) + + # --- Generate fixed effects, true effects, outcomes --- + group_fe = rng.normal(0, group_fe_sd, n_groups) + if heterogeneous_effects: + true_effects = rng.normal(treatment_effect, effect_sd, (n_groups, n_periods)) + else: + true_effects = np.full((n_groups, n_periods), float(treatment_effect)) + # Only treated cells have non-zero effect + true_effects = np.where(D == 1, true_effects, 0.0) + + period_arr = np.arange(n_periods) + Y = ( + 10.0 + + group_fe[:, None] + + time_trend * period_arr[None, :] + + true_effects + + rng.normal(0, noise_sd, (n_groups, n_periods)) + ) + + # --- Compute d_lag (NaN at period 0) --- + D_lag = np.full((n_groups, n_periods), np.nan) + D_lag[:, 1:] = D[:, :-1] + + # --- Vectorized switcher_type classification --- + treatment_flat = D.flatten() + d_lag_flat = D_lag.flatten() + switcher_type = np.full(n_groups * n_periods, "stable_1", dtype=object) + # Order matters: more specific masks last so they overwrite the default + mask_stable_0 = (d_lag_flat == 0) & (treatment_flat == 0) + mask_joiner = (d_lag_flat == 0) & (treatment_flat == 1) + mask_leaver = (d_lag_flat == 1) & (treatment_flat == 0) + mask_initial = np.isnan(d_lag_flat) + switcher_type[mask_stable_0] = "stable_0" + switcher_type[mask_joiner] = "joiner" + switcher_type[mask_leaver] = "leaver" + switcher_type[mask_initial] = "initial" # always wins for period 0 + + # --- Build the long-format DataFrame --- + return pd.DataFrame( + { + "group": np.repeat(np.arange(n_groups), n_periods), + "period": np.tile(period_arr, n_groups), + "treatment": treatment_flat, + "outcome": Y.flatten(), + "true_effect": true_effects.flatten(), + "d_lag": d_lag_flat, + "switcher_type": switcher_type, + } + ) diff --git a/docs/api/chaisemartin_dhaultfoeuille.rst b/docs/api/chaisemartin_dhaultfoeuille.rst new file mode 100644 index 000000000..71ee83e87 --- /dev/null +++ b/docs/api/chaisemartin_dhaultfoeuille.rst @@ -0,0 +1,231 @@ +de Chaisemartin-D'Haultfœuille (dCDH) DiD +============================================ + +The only modern staggered DiD estimator in diff-diff that handles +**non-absorbing (reversible) treatments** — treatment may switch on AND +off over time. + +This module implements the methodology from de Chaisemartin & D'Haultfœuille +(2020), "Two-Way Fixed Effects Estimators with Heterogeneous Treatment +Effects", *American Economic Review*. Phase 1 ships the contemporaneous- +switch estimator ``DID_M`` from the AER 2020 paper, which is mathematically +identical to ``DID_1`` (horizon ``l = 1``) of the dynamic companion paper +(de Chaisemartin & D'Haultfœuille, 2024, NBER WP 29873). The Phase 1 class +is forward-compatible with later phases — Phase 2 will add multi-horizon +event-study output ``DID_l`` for ``l > 1`` on the same class, and Phase 3 +will add covariate adjustment. + +The estimator: + +1. Aggregates individual-level panel data to ``(group, time)`` cells +2. Drops multi-switch groups by default (matches R ``DIDmultiplegtDYN``) +3. Excludes singleton-baseline groups from the variance computation only (footnote 15 of the dynamic paper) +4. Computes per-period joiner (``DID_{+,t}``) and leaver (``DID_{-,t}``) + contributions via Theorem 3 of the AER 2020 paper +5. Aggregates them into ``DID_M``, the joiners-only ``DID_+``, and the + leavers-only ``DID_-`` +6. Computes the single-lag placebo ``DID_M^pl`` +7. Optionally computes the TWFE decomposition diagnostic from Theorem 1 + (per-cell weights, fraction negative, ``sigma_fe``) +8. Inference uses the cohort-recentered analytical plug-in variance from + Web Appendix Section 3.7.3 of the dynamic paper, optionally + complemented by a multiplier bootstrap clustered at the group level + +**When to use ChaisemartinDHaultfoeuille:** + +- Treatment can switch on **and** off over time (e.g., marketing campaigns, + seasonal promotions, on/off policy cycles) +- You need separate joiners (``DID_+``) and leavers (``DID_-``) views, plus + the aggregate ``DID_M`` +- You want a built-in placebo and a TWFE decomposition diagnostic computed + on the data you pass in (pre-filter) for direct comparison against + ``DID_M``. The fitted TWFE diagnostic uses the FULL pre-filter cell + sample (matching :func:`twowayfeweights`); when ``fit()`` drops groups + via the ragged-panel or ``drop_larger_lower`` filters, a ``UserWarning`` + is emitted to make the divergence from the post-filter ``DID_M`` sample + explicit. See REGISTRY.md ``ChaisemartinDHaultfoeuille`` ``Note (TWFE + diagnostic sample contract)`` for the rationale. +- You want a Python implementation that matches R ``DIDmultiplegtDYN`` at + ``l = 1`` on cell-aggregated input (see REGISTRY.md for documented + deviations on individual-level inputs with uneven cell sizes) + +All other staggered estimators in diff-diff (:class:`~diff_diff.CallawaySantAnna`, +:class:`~diff_diff.SunAbraham`, :class:`~diff_diff.ImputationDiD`, +:class:`~diff_diff.TwoStageDiD`, :class:`~diff_diff.EfficientDiD`, +:class:`~diff_diff.WooldridgeDiD`) assume treatment is **absorbing** — +once treated, stays treated. ``ChaisemartinDHaultfoeuille`` is the only +library option for non-absorbing treatments. + +**Phase 1 panel requirements (deviation from R DIDmultiplegtDYN):** + +- Every group must have an observation at the **first global period** + (the panel's earliest time value). Groups missing this baseline raise + ``ValueError`` with the offending group IDs. +- Groups with **interior period gaps** (missing observations between + their first and last observed period) are dropped with a + ``UserWarning``. +- **Terminal missingness** (groups observed at the baseline but missing + one or more later periods — early exit / right-censoring) is supported. + The group contributes from its observed periods only, masked out of + the missing transitions by the per-period ``present`` guard in the + variance computation. +- This is a Phase 1 limitation relative to R ``DIDmultiplegtDYN``, which + supports unbalanced panels with documented missing-treatment-before- + first-switch handling. **Workaround:** pre-process your panel to + back-fill the baseline (or drop late-entry groups before fitting), or + use R until a future phase lifts the restriction. See the + ``Note (deviation from R DIDmultiplegtDYN)`` block in + ``docs/methodology/REGISTRY.md`` for the rationale and the exact + defensive guards that make terminal missingness safe. + +**References:** + +- de Chaisemartin, C. & D'Haultfœuille, X. (2020). Two-Way Fixed Effects + Estimators with Heterogeneous Treatment Effects. *American Economic + Review*, 110(9), 2964-2996. +- de Chaisemartin, C. & D'Haultfœuille, X. (2022, revised 2024). + Difference-in-Differences Estimators of Intertemporal Treatment + Effects. NBER Working Paper 29873. + +.. module:: diff_diff.chaisemartin_dhaultfoeuille + +ChaisemartinDHaultfoeuille +-------------------------- + +Main estimator class for de Chaisemartin-D'Haultfœuille (dCDH) DiD estimation. +The alias :class:`~diff_diff.DCDH` is also available. + +.. autoclass:: diff_diff.ChaisemartinDHaultfoeuille + :members: + :undoc-members: + :show-inheritance: + :inherited-members: + + .. rubric:: Methods + + .. autosummary:: + + ~ChaisemartinDHaultfoeuille.fit + ~ChaisemartinDHaultfoeuille.get_params + ~ChaisemartinDHaultfoeuille.set_params + +ChaisemartinDHaultfoeuilleResults +--------------------------------- + +Results container for dCDH estimation. + +.. autoclass:: diff_diff.ChaisemartinDHaultfoeuilleResults + :members: + :undoc-members: + :show-inheritance: + + .. rubric:: Methods + + .. autosummary:: + + ~ChaisemartinDHaultfoeuilleResults.summary + ~ChaisemartinDHaultfoeuilleResults.print_summary + ~ChaisemartinDHaultfoeuilleResults.to_dataframe + +DCDHBootstrapResults +-------------------- + +Multiplier-bootstrap inference results, populated when ``n_bootstrap > 0``. + +.. autoclass:: diff_diff.DCDHBootstrapResults + :members: + :undoc-members: + :show-inheritance: + +Convenience Function +-------------------- + +.. autofunction:: diff_diff.chaisemartin_dhaultfoeuille + +Standalone TWFE Decomposition Diagnostic +---------------------------------------- + +The TWFE decomposition diagnostic from Theorem 1 of de Chaisemartin & +D'Haultfœuille (2020) is also available as a standalone function for +users who want the diagnostic without fitting the full estimator. It +returns per-cell weights, the fraction of treated cells with negative +weights, and ``sigma_fe`` — the smallest standard deviation of per-cell +treatment effects that could flip the sign of the plain TWFE coefficient. + +.. autofunction:: diff_diff.twowayfeweights + +.. autoclass:: diff_diff.chaisemartin_dhaultfoeuille.TWFEWeightsResult + :members: + +Example Usage +------------- + +Basic usage with reversible treatment:: + + from diff_diff import ChaisemartinDHaultfoeuille + from diff_diff.prep import generate_reversible_did_data + + data = generate_reversible_did_data( + n_groups=80, n_periods=6, pattern="single_switch", seed=42, + ) + + est = ChaisemartinDHaultfoeuille() + results = est.fit( + data, + outcome="outcome", + group="group", + time="period", + treatment="treatment", + ) + results.print_summary() + +Joiners and leavers views:: + + print(f"DID_M (overall): {results.overall_att:.3f}") + print(f"DID_+ (joiners): {results.joiners_att:.3f}") + print(f"DID_- (leavers): {results.leavers_att:.3f}") + print(f"Placebo (DID^pl): {results.placebo_effect:.3f}") + +Per-period decomposition:: + + for t, cell in results.per_period_effects.items(): + print( + f"t={t}: DID+={cell['did_plus_t']:.3f} " + f"({cell['n_10_t']} joiners, {cell['n_00_t']} stable_0 controls)" + ) + +Multiplier bootstrap inference:: + + est = ChaisemartinDHaultfoeuille( + n_bootstrap=999, bootstrap_weights="rademacher", seed=42, + ) + results = est.fit( + data, outcome="outcome", group="group", + time="period", treatment="treatment", + ) + # When n_bootstrap > 0, the top-level overall_*/joiners_*/leavers_* + # p-value and conf_int fields hold percentile-based bootstrap + # inference (not normal-theory recomputations from the bootstrap SE). + # The t-stat is computed from the SE in both cases. See REGISTRY.md + # `Note (bootstrap inference surface)` for the full contract. + print(f"Top-level p-value (bootstrap): {results.overall_p_value:.4f}") + print(f"Top-level CI (bootstrap): {results.overall_conf_int}") + print(f"bootstrap_results.overall_se: {results.bootstrap_results.overall_se:.3f}") + print(f"bootstrap_results.overall_ci: {results.bootstrap_results.overall_ci}") + +Standalone TWFE diagnostic (without fitting the full estimator):: + + from diff_diff import twowayfeweights + + diagnostic = twowayfeweights( + data, outcome="outcome", group="group", time="period", treatment="treatment", + ) + print(f"Plain TWFE coefficient: {diagnostic.beta_fe:.3f}") + print(f"Fraction of negative weights: {diagnostic.fraction_negative:.3f}") + print(f"sigma_fe (sign-flipping threshold): {diagnostic.sigma_fe:.3f}") + +The ``DCDH`` alias:: + + from diff_diff import DCDH + + est = DCDH() # equivalent to ChaisemartinDHaultfoeuille() diff --git a/docs/api/index.rst b/docs/api/index.rst index 92c506e05..3d08dc98c 100644 --- a/docs/api/index.rst +++ b/docs/api/index.rst @@ -17,6 +17,7 @@ Core estimator classes for DiD analysis: diff_diff.MultiPeriodDiD diff_diff.SyntheticDiD diff_diff.CallawaySantAnna + diff_diff.ChaisemartinDHaultfoeuille diff_diff.SunAbraham diff_diff.ImputationDiD diff_diff.StackedDiD @@ -44,6 +45,8 @@ Result containers returned by estimators: diff_diff.CallawaySantAnnaResults diff_diff.CSBootstrapResults diff_diff.GroupTimeEffect + diff_diff.ChaisemartinDHaultfoeuilleResults + diff_diff.DCDHBootstrapResults diff_diff.SunAbrahamResults diff_diff.SABootstrapResults diff_diff.ImputationDiDResults @@ -228,6 +231,7 @@ Estimators estimators staggered + chaisemartin_dhaultfoeuille imputation stacked_did triple_diff diff --git a/docs/api/prep.rst b/docs/api/prep.rst index 7491c0856..9a4b09ce1 100644 --- a/docs/api/prep.rst +++ b/docs/api/prep.rst @@ -91,6 +91,36 @@ Generate synthetic continuous treatment DiD data with known dose-response. .. autofunction:: diff_diff.generate_continuous_did_data +generate_reversible_did_data +~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Generate synthetic **reversible-treatment** panel data — treatment can switch on +and off over time. Use this with :class:`~diff_diff.ChaisemartinDHaultfoeuille` +for testing the dCDH estimator on non-absorbing treatments. + +.. autofunction:: diff_diff.generate_reversible_did_data + +Example +^^^^^^^ + +.. code-block:: python + + from diff_diff import generate_reversible_did_data, ChaisemartinDHaultfoeuille + + data = generate_reversible_did_data( + n_groups=80, + n_periods=6, + pattern="single_switch", # or "joiners_only", "leavers_only", "mixed_single_switch" + treatment_effect=2.0, + seed=42, + ) + + est = ChaisemartinDHaultfoeuille() + results = est.fit( + data, outcome="outcome", group="group", + time="period", treatment="treatment", + ) + Indicator Creation ------------------ diff --git a/docs/choosing_estimator.rst b/docs/choosing_estimator.rst index 932dc4a33..44871a36f 100644 --- a/docs/choosing_estimator.rst +++ b/docs/choosing_estimator.rst @@ -23,25 +23,30 @@ Start here and follow the questions: - **No** → Go to question 2 - **Yes** → Use :class:`~diff_diff.ContinuousDiD` -2. **Is treatment staggered?** (Different units treated at different times) +2. **Can treatment switch on AND off?** (Reversible / non-absorbing treatment — e.g., marketing campaigns, seasonal promotions, on/off policy cycles) - - **No** → Go to question 3 + - **No (treatment is absorbing — once treated, stays treated)** → Go to question 3 + - **Yes** → Use :class:`~diff_diff.ChaisemartinDHaultfoeuille` — the only library estimator that handles non-absorbing treatments + +3. **Is treatment staggered?** (Different units treated at different times) + + - **No** → Go to question 4 - **Yes** → Use :class:`~diff_diff.CallawaySantAnna` (or :class:`~diff_diff.EfficientDiD` for tighter SEs under PT-All) - **Yes, and you suspect homogeneous effects** → Use :class:`~diff_diff.ImputationDiD` or :class:`~diff_diff.TwoStageDiD` for tighter CIs - **Yes, with nonlinear outcome (binary/count)** → Use :class:`~diff_diff.WooldridgeDiD` with ``method='logit'`` or ``method='poisson'`` - **Want to diagnose TWFE bias?** → Use :class:`~diff_diff.BaconDecomposition` first -3. **Do you have panel data?** (Multiple observations per unit over time) +4. **Do you have panel data?** (Multiple observations per unit over time) - **No** → Use :class:`~diff_diff.DifferenceInDifferences` (basic 2x2) - - **Yes** → Go to question 4 + - **Yes** → Go to question 5 -4. **Do you need period-specific effects?** (Event study design) +5. **Do you need period-specific effects?** (Event study design) - **No** → Use :class:`~diff_diff.TwoWayFixedEffects` - **Yes** → Use :class:`~diff_diff.MultiPeriodDiD` -5. **Is your treated group small?** (Few treated units, many controls) +6. **Is your treated group small?** (Few treated units, many controls) - Consider :class:`~diff_diff.SyntheticDiD` for better pre-treatment fit @@ -72,6 +77,10 @@ Quick Reference - Staggered adoption, heterogeneous timing - Conditional parallel trends - Group-time ATT(g,t), aggregations + * - ``ChaisemartinDHaultfoeuille`` + - Reversible / non-absorbing treatments (only library option) + - Parallel trends + A5 (no crossing) + A11 (stable controls) + - DID_M, joiners/leavers split, placebo, TWFE diagnostic * - ``SyntheticDiD`` - Few treated units, many controls - Synthetic parallel trends @@ -217,6 +226,78 @@ This is the recommended estimator for most applied work with staggered adoption. covariates=['x1', 'x2'], aggregate='event_study') event_study_df = es.to_dataframe('event_study') +Reversible (Non-Absorbing) Treatment +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Use :class:`~diff_diff.ChaisemartinDHaultfoeuille` (alias :class:`~diff_diff.DCDH`) when: + +- Treatment can switch on **and** off over time (e.g., marketing campaigns, + seasonal promotions, on/off policy cycles) +- You need separate joiners (``DID_+``) and leavers (``DID_-``) views, plus + the aggregate ``DID_M`` +- You want a built-in placebo and a TWFE decomposition diagnostic computed + on the data you pass in (pre-filter) for direct comparison against + ``DID_M`` + +This is **the only library estimator that handles non-absorbing treatments**. +All other staggered estimators +(:class:`~diff_diff.CallawaySantAnna`, :class:`~diff_diff.SunAbraham`, +:class:`~diff_diff.ImputationDiD`, :class:`~diff_diff.TwoStageDiD`, +:class:`~diff_diff.EfficientDiD`, :class:`~diff_diff.WooldridgeDiD`) assume +treatment is absorbing — once treated, stays treated. + +Phase 1 ships the contemporaneous-switch ``DID_M`` from de Chaisemartin & +D'Haultfœuille (2020), which is mathematically identical to ``DID_1`` +(horizon ``l = 1``) of their dynamic companion paper. Phase 2 will add +multi-horizon event-study output ``DID_l`` for ``l > 1``; Phase 3 will add +covariate adjustment. + +.. code-block:: python + + from diff_diff import ChaisemartinDHaultfoeuille + from diff_diff.prep import generate_reversible_did_data + + data = generate_reversible_did_data(n_groups=80, n_periods=6, seed=42) + + est = ChaisemartinDHaultfoeuille() + results = est.fit( + data, + outcome="outcome", + group="group", + time="period", + treatment="treatment", + ) + results.print_summary() + + print(f"DID_M (overall): {results.overall_att:.3f}") + print(f"DID_+ (joiners): {results.joiners_att:.3f}") + print(f"DID_- (leavers): {results.leavers_att:.3f}") + print(f"Placebo: {results.placebo_effect:.3f}") + +.. note:: + + By default, the estimator drops groups whose treatment switches more + than once before estimation (``drop_larger_lower=True``, matching the R + ``DIDmultiplegtDYN`` reference). This is required for the analytical + variance formula to be consistent with the point estimate. Each drop + emits an explicit warning. + +.. note:: + + The Phase 1 placebo SE is intentionally ``NaN`` with a warning. The + dynamic companion paper Section 3.7.3 derives the cohort-recentered + analytical variance for ``DID_l`` only — not for the placebo + ``DID_M^pl``. Phase 2 will add multiplier-bootstrap support for the + placebo. Until then, the placebo point estimate is meaningful but its + inference fields stay NaN-consistent even when ``n_bootstrap > 0`` + (bootstrap currently covers ``DID_M``, ``DID_+``, and ``DID_-`` only). + +.. note:: + + ``ChaisemartinDHaultfoeuille`` does not yet support ``survey_design``; + passing it raises ``NotImplementedError``. Survey integration is + deferred to a separate effort after Phases 2 and 3 ship. + Synthetic DiD ~~~~~~~~~~~~~ @@ -648,6 +729,11 @@ estimation. The depth of support varies by estimator: - Full - Full - Multiplier at PSU + * - ``ChaisemartinDHaultfoeuille`` + - -- + - -- + - -- + - -- * - ``TripleDifference`` - pweight only - Full diff --git a/docs/doc-deps.yaml b/docs/doc-deps.yaml index b4e045d06..b61a86a9c 100644 --- a/docs/doc-deps.yaml +++ b/docs/doc-deps.yaml @@ -48,6 +48,10 @@ groups: - diff_diff/efficient_did_covariates.py - diff_diff/efficient_did_results.py - diff_diff/efficient_did_weights.py + chaisemartin_dhaultfoeuille: + - diff_diff/chaisemartin_dhaultfoeuille.py + - diff_diff/chaisemartin_dhaultfoeuille_bootstrap.py + - diff_diff/chaisemartin_dhaultfoeuille_results.py continuous_did: - diff_diff/continuous_did.py - diff_diff/continuous_did_bspline.py @@ -252,6 +256,34 @@ sources: - path: docs/benchmarks.rst type: performance + # ── ChaisemartinDHaultfoeuille (chaisemartin_dhaultfoeuille group) ── + + diff_diff/chaisemartin_dhaultfoeuille.py: + drift_risk: high + docs: + - path: docs/methodology/REGISTRY.md + section: "ChaisemartinDHaultfoeuille" + type: methodology + note: "Single source of truth for dCDH methodology, equations, deviations from R, and edge cases" + - path: docs/api/chaisemartin_dhaultfoeuille.rst + type: api_reference + - path: README.md + section: "ChaisemartinDHaultfoeuille" + type: user_guide + - path: docs/llms-full.txt + section: "ChaisemartinDHaultfoeuille" + type: user_guide + - path: docs/llms.txt + type: user_guide + - path: docs/choosing_estimator.rst + type: user_guide + - path: docs/practitioner_decision_tree.rst + section: "Reversible Treatment" + type: user_guide + - path: ROADMAP.md + section: "de Chaisemartin-D'Haultfœuille (dCDH) Estimator" + type: roadmap + # ── ContinuousDiD (continuous_did group) ────────────────────��─────── diff_diff/continuous_did.py: diff --git a/docs/llms-full.txt b/docs/llms-full.txt index 5a624fa21..dfc962332 100644 --- a/docs/llms-full.txt +++ b/docs/llms-full.txt @@ -228,6 +228,92 @@ results.print_summary() plot_event_study(results) ``` +### ChaisemartinDHaultfoeuille + +de Chaisemartin & D'Haultfœuille (2020) `DID_M` estimator for **non-absorbing (reversible) treatments**. The only library estimator that handles treatments which can switch on AND off over time. Phase 1 ships the contemporaneous-switch case `DID_M`, equivalently `DID_1` (horizon `l = 1`) of the dynamic companion paper (NBER WP 29873). Phase 2 will add multi-horizon event-study output `DID_l` for `l > 1` on the same class. + +```python +ChaisemartinDHaultfoeuille( + alpha: float = 0.05, + cluster: str | None = None, # Phase 1: must be None; non-None raises NotImplementedError + n_bootstrap: int = 0, # 0 = analytical SE only + bootstrap_weights: str = "rademacher", # "rademacher", "mammen", or "webb" + seed: int | None = None, + placebo: bool = True, # Auto-compute single-lag placebo + twfe_diagnostic: bool = True, # Auto-compute Theorem 1 TWFE decomposition + drop_larger_lower: bool = True, # Drop multi-switch groups (matches R DIDmultiplegtDYN) + rank_deficient_action: str = "warn", # Used by TWFE diagnostic OLS +) +``` + +**Alias:** `DCDH` + +**fit() parameters:** + +```python +est.fit( + data: pd.DataFrame, + outcome: str, + group: str, # Group identifier + time: str, + treatment: str, # Per-observation binary treatment + # ---- forward-compat (Phase 2 / 3) ---- + aggregate: str | None = None, # Phase 2: "event_study" + L_max: int | None = None, # Phase 2: max horizon + controls: list[str] | None = None, # Phase 3: DID^X covariates + trends_linear: bool | None = None, # Phase 3: DID^{fd} + trends_nonparam: Any | None = None, # Phase 3: DID^s + honest_did: bool = False, # Phase 3: HonestDiD integration + # ---- deferred (separate effort) ---- + survey_design: Any = None, +) -> ChaisemartinDHaultfoeuilleResults +``` + +All forward-compat parameters raise `NotImplementedError` with phase pointers in Phase 1. + +**Usage:** + +```python +from diff_diff import ChaisemartinDHaultfoeuille +from diff_diff.prep import generate_reversible_did_data + +data = generate_reversible_did_data( + n_groups=80, n_periods=6, pattern="single_switch", seed=42, +) + +est = ChaisemartinDHaultfoeuille() +results = est.fit( + data, outcome="outcome", group="group", + time="period", treatment="treatment", +) +results.print_summary() + +# Decomposition +print(f"DID_M (overall): {results.overall_att:.3f}") +print(f"DID_+ (joiners): {results.joiners_att:.3f}") +print(f"DID_- (leavers): {results.leavers_att:.3f}") +print(f"Placebo (DID^pl): {results.placebo_effect:.3f}") +``` + +**Standalone TWFE diagnostic** (without fitting the full estimator): + +```python +from diff_diff import twowayfeweights + +diagnostic = twowayfeweights( + data, outcome="outcome", group="group", time="period", treatment="treatment", +) +print(f"Plain TWFE coefficient: {diagnostic.beta_fe:.3f}") +print(f"Fraction of negative weights: {diagnostic.fraction_negative:.3f}") +print(f"sigma_fe (sign-flipping threshold): {diagnostic.sigma_fe:.3f}") +``` + +**Notes:** +- Validated against R `DIDmultiplegtDYN` v2.3.3 at horizon `l = 1` via `tests/test_chaisemartin_dhaultfoeuille_parity.py` +- Phase 1 placebo SE is intentionally `NaN` with a warning. The dynamic companion paper Section 3.7.3 derives the cohort-recentered analytical variance for `DID_l` only — not for the placebo `DID_M^pl`. Phase 2 will add multiplier-bootstrap support for the placebo. Until then, the placebo point estimate is meaningful but its inference fields stay NaN-consistent **even when `n_bootstrap > 0`** (bootstrap currently covers `DID_M`, `DID_+`, and `DID_-` only) +- The analytical CI is conservative under Assumption 8 (independent groups) of the dynamic companion paper, exact only under iid sampling +- Survey design (`survey_design`) is not yet supported and is deferred to a separate effort after all phases ship + ### SunAbraham Sun-Abraham (2021) interaction-weighted estimator for staggered DiD. diff --git a/docs/llms.txt b/docs/llms.txt index 6e1962863..e3f755ef3 100644 --- a/docs/llms.txt +++ b/docs/llms.txt @@ -2,7 +2,7 @@ > A Python library for Difference-in-Differences (DiD) causal inference analysis. Provides sklearn-like estimators with statsmodels-style summary output for econometric analysis. -diff-diff offers 16 estimators covering basic 2x2 DiD, modern staggered adoption methods, advanced panel estimators, nonlinear models, and diagnostic tools. It supports robust and cluster-robust standard errors, wild cluster bootstrap, formula and column-name interfaces, fixed effects (dummy and absorbed), complex survey designs (strata/PSU/FPC, replicate weights, design-based variance), and publication-ready output. The optional Rust backend accelerates compute-intensive estimators like Synthetic DiD and TROP. +diff-diff offers 17 estimators covering basic 2x2 DiD, modern staggered adoption methods, reversible (non-absorbing) treatments, advanced panel estimators, nonlinear models, and diagnostic tools. It supports robust and cluster-robust standard errors, wild cluster bootstrap, formula and column-name interfaces, fixed effects (dummy and absorbed), complex survey designs (strata/PSU/FPC, replicate weights, design-based variance), and publication-ready output. The optional Rust backend accelerates compute-intensive estimators like Synthetic DiD and TROP. - Install: `pip install diff-diff` - License: MIT @@ -54,6 +54,7 @@ Full practitioner guide: docs/llms-practitioner.txt - [TwoWayFixedEffects](https://diff-diff.readthedocs.io/en/stable/api/estimators.html): Panel data DiD with unit and time fixed effects via within-transformation or dummies - [MultiPeriodDiD](https://diff-diff.readthedocs.io/en/stable/api/estimators.html): Event study design with period-specific treatment effects for dynamic analysis - [CallawaySantAnna](https://diff-diff.readthedocs.io/en/stable/api/staggered.html): Callaway & Sant'Anna (2021) group-time ATT estimator for staggered adoption with aggregation +- [ChaisemartinDHaultfoeuille](https://diff-diff.readthedocs.io/en/stable/api/chaisemartin_dhaultfoeuille.html): de Chaisemartin & D'Haultfœuille (2020) `DID_M` estimator for **reversible (non-absorbing) treatments** — the only library option for treatments that switch on AND off (marketing campaigns, seasonal promotions, on/off policy cycles). Alias `DCDH`. - [SunAbraham](https://diff-diff.readthedocs.io/en/stable/api/staggered.html): Sun & Abraham (2021) interaction-weighted estimator for heterogeneity-robust event studies - [ImputationDiD](https://diff-diff.readthedocs.io/en/stable/api/imputation.html): Borusyak, Jaravel & Spiess (2024) imputation estimator — most efficient under homogeneous effects - [TwoStageDiD](https://diff-diff.readthedocs.io/en/stable/api/two_stage.html): Gardner (2022) two-stage estimator with GMM sandwich variance diff --git a/docs/methodology/REGISTRY.md b/docs/methodology/REGISTRY.md index 2b9b4479e..9da6214bf 100644 --- a/docs/methodology/REGISTRY.md +++ b/docs/methodology/REGISTRY.md @@ -10,6 +10,7 @@ This document provides the academic foundations and key implementation requireme - [TwoWayFixedEffects](#twowayfixedeffects) 2. [Modern Staggered Estimators](#modern-staggered-estimators) - [CallawaySantAnna](#callawaysantanna) + - [ChaisemartinDHaultfoeuille](#chaisemartindhaultfoeuille) - [ContinuousDiD](#continuousdid) - [SunAbraham](#sunabraham) - [ImputationDiD](#imputationdid) @@ -456,6 +457,148 @@ The multiplier bootstrap uses random weights w_i with E[w]=0 and Var(w)=1: --- +## ChaisemartinDHaultfoeuille + +**Primary sources:** +- [de Chaisemartin, C. & D'Haultfœuille, X. (2020). Two-Way Fixed Effects Estimators with Heterogeneous Treatment Effects. *American Economic Review*, 110(9), 2964-2996.](https://doi.org/10.1257/aer.20181169) +- [de Chaisemartin, C. & D'Haultfœuille, X. (2022, revised 2024). Difference-in-Differences Estimators of Intertemporal Treatment Effects. NBER Working Paper 29873.](https://www.nber.org/papers/w29873) — Web Appendix Section 3.7.3 contains the cohort-recentered plug-in variance formula implemented here. + +**Phase 1 scope:** Ships the contemporaneous-switch estimator `DID_M` from the AER 2020 paper, equivalently `DID_1` (horizon `l = 1`) of the dynamic companion paper. The full multi-phase rollout is in `ROADMAP.md`: Phase 2 adds dynamic horizons `DID_l` for `l > 1`, normalized estimators, cost-benefit aggregates, and sup-t bands; Phase 3 adds covariate adjustment (`DID^X`), group-specific linear trends (`DID^{fd}`), state-set-specific trends, and HonestDiD integration. Survey design support is deferred to a separate effort after all phases ship. **This is the only modern staggered estimator in the library that handles non-absorbing (reversible) treatments** — treatment can switch on AND off over time, making it the natural fit for marketing campaigns, seasonal promotions, on/off policy cycles. + +**Key implementation requirements:** + +*Assumption checks / warnings:* +- Treatment must be binary (0/1). Phase 3 will accept non-binary; Phase 1 raises `ValueError` for non-binary input. +- NaN values in `treatment` or `outcome` columns raise `ValueError` early in `fit()` (no silent drops). +- Treatment must be constant within each `(g, t)` cell. Within-cell-varying treatment (fractional `d_gt` after aggregation) raises `ValueError`. Pre-aggregate your data to constant binary cell-level treatment before fitting. Fuzzy DiD is deferred to a separate dCDH 2018 paper not covered by Phase 1. +- Multi-switch groups (those that switch treatment more than once across periods) are dropped before estimation when `drop_larger_lower=True` (the default, matching R `DIDmultiplegtDYN`). Each drop emits a warning with the count and example group IDs. See the multi-switch Note below. +- Singleton-baseline groups — groups whose `D_{g,1}` value is unique in the post-drop dataset — are excluded from the **variance computation only** (per footnote 15 of the dynamic paper, they have no cohort peer). They are **retained** in the point-estimate sample as period-based stable controls. Each emits a warning. See the singleton-baseline Note below. +- Never-switching groups (`S_g = 0`) participate in the variance computation when they serve as stable controls under the full influence function. The `n_groups_dropped_never_switching` results field is reported for backwards compatibility but the count no longer represents an actual exclusion. +- **Balanced-baseline panel required (deviation from R `DIDmultiplegtDYN`).** Every group must have an observation at the **first global period** (the panel's earliest time value); groups missing this baseline raise `ValueError` with the offending group IDs. Groups with **interior period gaps** (missing observations between their first and last observed period) are dropped with a `UserWarning`. **Terminal missingness** (groups observed at the baseline but missing one or more *later* periods) is **retained**: the group contributes from its observed periods only, masked out of the missing transitions by the per-period `present = (N_mat[:, t] > 0) & (N_mat[:, t-1] > 0)` guard. See the ragged-panel deviation Note below. +- **Period-index semantics.** The estimator operates on **sorted period indices**, not calendar dates. Per-period DIDs use `Y_{g,t} - Y_{g,t-1}` where `t-1` is the *previous observed period in the sorted panel*, not the previous calendar unit. A panel with periods `[2000, 2001, 2003]` (missing year 2002 for ALL groups) is treated as a valid 3-period panel where 2003 is the immediate successor of 2001. The estimator does NOT validate that periods are evenly spaced or that calendar gaps have been imputed. This matches the AER 2020 paper's Theorem 3, which defines transition sets by adjacent sorted periods without assuming calendar regularity, and is consistent with R `DIDmultiplegtDYN`'s behavior. If your data has calendar gaps that should be treated as missing periods rather than adjacent transitions, insert placeholder rows for the missing periods with the group's lagged treatment value and a reasonable imputed outcome (e.g., the group's last observed outcome), so the cell-aggregation step treats the gap as a stable-treatment period rather than a missing one. The validator rejects NaN in outcome and treatment columns, so placeholders must have finite values. +- Per-period Assumption 11 violations (joiners exist but no stable-untreated controls in some period, or leavers exist but no stable-treated controls) trigger zero-retention behavior with a consolidated warning. See the A11 Note below. + +*Estimator equations (Theorem 3 of AER 2020 / Section 3.7.2 of the dynamic paper):* + +Per-period DiDs at each switching period `t >= 2`: + +``` +DID_{+,t} = (1/N_{1,0,t}) * sum_{g in joiners(t)} (Y_{g,t} - Y_{g,t-1}) + - (1/N_{0,0,t}) * sum_{g in stable_0(t)} (Y_{g,t} - Y_{g,t-1}) + +DID_{-,t} = (1/N_{1,1,t}) * sum_{g in stable_1(t)} (Y_{g,t} - Y_{g,t-1}) + - (1/N_{0,1,t}) * sum_{g in leavers(t)} (Y_{g,t} - Y_{g,t-1}) +``` + +where `joiners(t)` are groups switching from `D_{g,t-1}=0` to `D_{g,t}=1`, `leavers(t)` are groups switching `1->0`, `stable_0(t)` are groups with `D_{g,t-1}=D_{g,t}=0`, and `stable_1(t)` are groups with `D_{g,t-1}=D_{g,t}=1`. **`N_{a,b,t}` is the COUNT of `(g, t)` cells in each transition state — not the sum of within-cell observation counts.** Each `(g, t)` cell contributes once to its transition's count regardless of how many original observations fed into the cell mean. The cell mean `Y_{g,t}` is computed at the cell-aggregation step via `groupby([group, time]).agg(y_gt=mean)`; the per-period DIDs use these cell means directly without further sample-size weighting. This matches the AER 2020 paper's cell-level notation for `N_{a,b,t}` as a count of transition-state cells (the paper can also be read as using observation sums; the equal-cell interpretation is the one implemented here). **Note (deviation from R `DIDmultiplegtDYN`):** On individual-level inputs with uneven `(group, time)` cell sizes, Python gives each cell **equal weight** (paper-literal cell-count weighting). R `DIDmultiplegtDYN`, absent an explicit weight variable, weights estimation by the number of observations in each cell (cell-size weighting). The two agree exactly on cell-aggregated input where every cell has the same number of observations. The Python parity tests in `tests/test_chaisemartin_dhaultfoeuille_parity.py` use the `generate_reversible_did_data()` generator, which produces exactly one observation per cell, so parity holds. The regression test `test_cell_count_weighting_unbalanced_input` in `tests/test_chaisemartin_dhaultfoeuille.py` explicitly pins the equal-cell contract. + +Aggregate `DID_M`: + +``` +N_S = sum_{t>=2} (N_{1,0,t} + N_{0,1,t}) +DID_M = (1/N_S) * sum_{t>=2} (N_{1,0,t} * DID_{+,t} + N_{0,1,t} * DID_{-,t}) +``` + +Joiners-only and leavers-only views (each weighted by its own switcher count): + +``` +DID_+ = sum_{t>=2} (N_{1,0,t} / sum_{t} N_{1,0,t}) * DID_{+,t} +DID_- = sum_{t>=2} (N_{0,1,t} / sum_{t} N_{0,1,t}) * DID_{-,t} +``` + +Single-lag placebo (AER 2020 placebo specification, same section as Theorem 3) — applies the same Theorem 3 logic to `Y_{g,t-1} - Y_{g,t-2}` on cells with 3-period histories: + +``` +DID_M^pl = (1/N_S^pl) * sum_{t>=3} ( + N_{1,0,t} * [(Y_{g,t-1} - Y_{g,t-2})_{joiners} - ...] + + N_{0,1,t} * [(Y_{g,t-1} - Y_{g,t-2})_{stable_1} - ...] + ) +``` + +*Standard errors (Web Appendix Section 3.7.3 of the dynamic companion paper):* + +Default: cohort-recentered analytical plug-in variance, evaluated at horizon `l = 1`. Cohorts are defined by the triple `(D_{g,1}, F_g, S_g)` (baseline treatment, first-switch period, switch direction). Each group's per-period role weights (joiner, stable_0, leaver, stable_1) sum to a per-group `U^G_g` value via the full `Lambda^G_{g,l=1}` weight vector from Section 3.7.2 of the dynamic paper: + +``` +N_S * DID_M = sum_t [ + sum_{g in joiners(t)} (Y_{g,t} - Y_{g,t-1}) + - (N_{1,0,t} / N_{0,0,t}) * sum_{g in stable_0(t)} (Y_{g,t} - Y_{g,t-1}) + + (N_{0,1,t} / N_{1,1,t}) * sum_{g in stable_1(t)} (Y_{g,t} - Y_{g,t-1}) + - sum_{g in leavers(t)} (Y_{g,t} - Y_{g,t-1}) +] +``` + +Reading off the coefficient on each `(Y_{g,t} - Y_{g,t-1})` gives the per-cell role weight, which sums across periods to: + +``` +U^G_g = sum_t lambda^G_{g,t} * (Y_{g,t} - Y_{g,t-1}) # full IF +U_bar_k = (1/|C_k|) * sum_{g in C_k} U^G_g # cohort-conditional mean +sigma_hat^2 = sum_g (U^G_g - U_bar_{cohort(g)})^2 / N_l +SE = sqrt(sigma_hat^2 / N_l) +``` + +Each switching group typically contributes from MULTIPLE periods: its own switch period plus every period where it serves as a stable control for another cohort's switch. Never-switching groups can also have non-zero `U^G_g` when they serve as stable controls. Singleton-baseline groups (footnote 15 of dynamic paper) are excluded from this sum because they have no cohort peer. + +The cohort recentering is critical: subtracting cohort-conditional means is **not** the same as subtracting a single grand mean. The implementation has a dedicated regression test (`test_cohort_recentering_not_grand_mean`) that computes both formulas on a designed DGP and asserts they differ materially. + +Alternative: Multiplier bootstrap clustered at group via the `n_bootstrap` parameter. Available weight distributions: `"rademacher"` (default), `"mammen"`, `"webb"`. The bootstrap is a library extension beyond the original papers and is provided for consistency with `CallawaySantAnna` / `ImputationDiD` / `TwoStageDiD`. + +*Edge cases:* +- **No switchers in data** (after filtering): raises `ValueError` with a clear message indicating which filters dropped which groups. +- **No joiners** (only leavers in data): `joiners_available = False`, all `joiners_*` fields are `NaN`. Symmetric for `leavers_available = False`. +- **`T < 3`**: placebo cannot be computed; `placebo_available = False` with a `UserWarning`. +- **NaN inference**: `safe_inference()` produces NaN-consistent inference fields (t-stat, p-value, conf int) when SE is non-finite or zero. `assert_nan_inference()` is used in tests to enforce consistency. +- **TWFE diagnostic with zero denominator**: when `sum(d_gt - d_bar)^2 == 0` (e.g., all cells have identical treatment), the diagnostic returns NaN for `beta_fe` and `sigma_fe` with a `UserWarning`. The diagnostic is non-fatal — it does not block the main estimation. +- **`placebo=False`** (gating): the results object still exposes `placebo_*` fields, but with `NaN` values and `placebo_available = False`. This keeps the API surface stable. + +- **Note:** The analytical CI is **conservative** under Assumption 8 (independent groups) of the dynamic companion paper, and exact only under iid sampling. This is documented as a deliberate deviation from "default nominal coverage". The bootstrap CI uses the same conservative weighting and is provided for users who want a non-asymptotic alternative. + +- **Note:** Phase 1 placebo SE is intentionally `NaN` with a `UserWarning`. The dynamic companion paper Section 3.7.3 derives the cohort-recentered analytical variance for `DID_l` only — not for the placebo `DID_M^pl`. Phase 2 will add multiplier-bootstrap support for the placebo via the dynamic paper's machinery. Until then, the placebo point estimate is meaningful but its inference fields stay NaN-consistent **even when `n_bootstrap > 0`**: the bootstrap path computes SEs for `DID_M`, `DID_+`, and `DID_-`, but `placebo_se`, `placebo_t_stat`, `placebo_p_value`, and `placebo_conf_int` remain `NaN` because the placebo's influence function machinery is deferred to Phase 2. + +- **Note:** When every variance-eligible group forms its own `(D_{g,1}, F_g, S_g)` cohort (a degenerate small-panel case where the cohort framework has zero degrees of freedom), the cohort-recentered plug-in formula is unidentified: cohort recentering subtracts the cohort mean from each group's `U^G_g`, and for singleton cohorts the centered value is exactly zero, so the centered influence function vector collapses to all zeros. The estimator returns `overall_se = NaN` with a `UserWarning` rather than silently collapsing to `0.0` (which would falsely imply infinite precision). The `DID_M` point estimate remains well-defined. The bootstrap path inherits the same degeneracy on these panels — the multiplier weights act on an all-zero vector, so the bootstrap distribution is also degenerate. **Deviation from R `DIDmultiplegtDYN`:** R returns a non-zero SE on the canonical 4-group worked example via small-sample sandwich machinery that Python does not implement. Both responses are valid for a degenerate case; Python's `NaN`+warning is the safer default. To get a non-degenerate SE, include more groups so cohorts have peers (real-world panels typically have `G >> K`). + +- **Note (Phase 1 cluster contract):** `ChaisemartinDHaultfoeuille` always clusters at the group level. The cohort-recentered analytical SE plug-in operates on per-group influence-function values (one `U^G_g` per group); the multiplier bootstrap generates one weight per group; both inference paths cluster at the user's `group` column with no other option. The constructor accepts `cluster=None` (the default and only supported value); passing any non-`None` value raises `NotImplementedError` with a Phase 1 pointer at construction time (and the same gate fires from `set_params`). Custom clustering at a coarser or finer level than the group is reserved for a future phase. The matching test is `test_cluster_parameter_raises_not_implemented` in `tests/test_chaisemartin_dhaultfoeuille.py::TestForwardCompatGates`. + +- **Note (bootstrap inference surface):** When `n_bootstrap > 0`, the top-level `results.overall_p_value` / `results.overall_conf_int` (and joiners/leavers analogues) hold **percentile-based bootstrap inference** computed by the multiplier bootstrap, NOT normal-theory recomputations from the bootstrap SE. The t-stat (`overall_t_stat`, etc.) is computed from the SE via `safe_inference()[0]` to satisfy the project's anti-pattern rule (never compute `t = effect / se` inline) — bootstrap does not define an alternative t-stat semantic for percentile bootstrap, so the SE-based t-stat is the natural choice. `event_study_effects[1]`, `summary()`, `to_dataframe()`, `is_significant`, and `significance_stars` all read from these top-level fields and therefore reflect the bootstrap inference automatically. The library precedent for this propagation is `imputation.py:790-805`, `two_stage.py:778-787`, and `efficient_did.py:1009-1013`. The placebo path is unchanged: placebo bootstrap is deferred to Phase 2 (see the placebo SE Note above), so `placebo_p_value` and `placebo_conf_int` stay NaN even when `n_bootstrap > 0`. The matching test is `test_bootstrap_p_value_and_ci_propagated_to_top_level` in `tests/test_chaisemartin_dhaultfoeuille.py::TestBootstrap`. + +- **Note:** Placebo Assumption 11 violations (placebo joiners exist but no 3-period stable_0 controls, or symmetric for leavers/stable_1) trigger zero-retention in the placebo numerator AND emit a consolidated `Placebo (DID_M^pl) Assumption 11 violations` warning from `fit()`, mirroring the main DID path's contract documented above. The zeroed placebo periods retain their switcher counts in the placebo `N_S^pl` denominator, biasing `DID_M^pl` toward zero in the offending direction (matching the placebo paper convention). + +- **Note (TWFE diagnostic sample contract):** The fitted `results.twfe_weights` / `results.twfe_fraction_negative` / `results.twfe_sigma_fe` / `results.twfe_beta_fe` are computed on the **FULL pre-filter cell sample** — the data the user passed in, after `_validate_and_aggregate_to_cells()` runs but **before** the ragged-panel validation (Step 5b) and the multi-switch filter (`drop_larger_lower`, Step 6). They do NOT describe the post-filter estimation sample used by `overall_att`, `results.groups`, and the inference fields. `fit()` has three sample-shaping filters in total: (1) interior-gap drops in Step 5b, (2) multi-switch drops in Step 6, and (3) the singleton-baseline filter in Step 7. Filters (1) and (2) actually shrink the point-estimate sample, so when either fires, the fitted TWFE diagnostic and `overall_att` describe **different samples** and the estimator emits a `UserWarning` explaining the divergence with explicit counts. Filter (3) is **variance-only** — singleton-baseline groups remain in the point-estimate sample as period-based stable controls (see the singleton-baseline Note above) — so it does NOT create a fitted-vs-`overall_att` mismatch and does NOT trigger the divergence warning. Rationale for the pre-filter design: the TWFE diagnostic answers "what would the plain TWFE estimator say on the data you passed in?" — not "what would TWFE say on the data dCDH actually used after filtering?" — so users comparing TWFE vs dCDH on a fixed input can do so without an interaction effect from the dCDH-specific filters. The standalone `twowayfeweights()` function uses the same pre-filter sample, so the fitted and standalone APIs always produce identical numbers on the same input. To reproduce the dCDH estimation sample for an external TWFE comparison, pre-process your data to drop the multi-switch and interior-gap groups before fitting (the warning lists offending IDs). The matching tests are `test_twfe_pre_filter_contract_with_interior_gap_drop` and `test_twfe_pre_filter_contract_with_multi_switch_drop` in `tests/test_chaisemartin_dhaultfoeuille.py`. + +- **Note:** By default (`drop_larger_lower=True`), the estimator drops groups whose treatment switches more than once before estimation. This matches R `DIDmultiplegtDYN`'s default and is required for the analytical variance formula (Web Appendix Section 3.7.3 of the dynamic paper, which assumes Assumption 5 / no-crossing) to be consistent with the AER 2020 Theorem 3 point estimate. Both formulas operate on the same post-drop dataset. Setting `drop_larger_lower=False` is supported for diagnostic comparison but produces an inconsistent estimator-variance pairing for any multi-switch groups present, and emits an explicit warning. + +- **Note:** When Assumption 11 (existence of stable controls) is violated for some period `t` — i.e., joiners exist but no stable-untreated controls, or leavers exist but no stable-treated controls — `DID_{+,t}` (or `DID_{-,t}`) is set to zero by paper convention, and the period's switcher count is **retained** in the `N_S` denominator. This means the affected period contributes a zero to the numerator with a non-zero weight in the denominator, biasing `DID_M` toward zero in the offending direction. Users can detect this by inspecting `results.per_period_effects[t]['did_plus_t_a11_zeroed']` (or `did_minus_t_a11_zeroed`) or the consolidated `fit()` warning. This matches the AER 2020 Theorem 3 paper convention and the worked example arithmetic. + +- **Note:** Groups whose baseline treatment value `D_{g,1}` is unique in the post-drop panel (not shared by any other group) are excluded from the **variance computation only** per footnote 15 of the dynamic companion paper. They have no cohort peer for the cohort-recentered plug-in formula. They are **retained in the point-estimate sample** as period-based stable controls (Python's documented period-vs-cohort interpretation). The dropped count is stored on `results.n_groups_dropped_singleton_baseline`, a warning lists example group IDs, and the warning text explicitly states "VARIANCE computation only" so users know the filter does not change `DID_M`. + +- **Note (deviation from R DIDmultiplegtDYN):** Python uses **period-based** stable-control sets — `stable_0(t)` is any cell with `D_{g,t-1} = D_{g,t} = 0` regardless of baseline `D_{g,1}`, and similarly for `stable_1(t)`. R `DIDmultiplegtDYN` uses **cohort-based** stable-control sets that additionally require `D_{g,1}` to match the side. Python's definition matches the AER 2020 Theorem 3 cell-count notation `N_{0,0,t}` and `N_{1,1,t}` literally; R's definition matches the dynamic companion paper's cohort `(D_{g,1}, F_g, S_g)` framework. The two definitions agree exactly on (a) panels containing only joiners, (b) panels containing only leavers, (c) the hand-calculable 4-group worked example, or (d) any panel where no joiner's post-switch state overlaps a period when leavers are switching. They disagree by O(1%) on the **point estimate** when both joiners and leavers exist AND some joiners' post-switch cells could serve as leavers' controls (or vice versa). After the Round 2 fix that implemented the full `Lambda^G_{g,l=1}` influence function, the **standard error** parity gap on pure-direction scenarios narrowed from ~18% to ~3%. The R parity tests in `tests/test_chaisemartin_dhaultfoeuille_parity.py` use a tight `1e-4` tolerance for pure-direction point estimates, a 5% rtol for pure-direction SEs, and a 2.5% tolerance for mixed-direction point estimates (with the SE check skipped on mixed scenarios because the period-vs-cohort point-estimate deviation cascades into the variance). + +- **Note (deviation from R DIDmultiplegtDYN):** Phase 1 requires panels with a **balanced baseline** (every group observed at the first global period) and **no interior period gaps**. The Step 5b validation in `fit()` enforces this contract: groups missing the baseline raise `ValueError`; groups with interior gaps are dropped with a `UserWarning`; groups with **terminal missingness** (early exit / right-censoring — observed at the baseline but missing one or more later periods) are retained and contribute from their observed periods only. R `DIDmultiplegtDYN` accepts unbalanced panels with documented missing-treatment-before-first-switch handling. Python's restriction is a Phase 1 limitation: the cohort enumeration uses `D_{g,1}` as the canonical baseline (so the baseline observation must exist) and the first-switch detection walks adjacent observed periods (so interior gaps create ambiguous transition counts). Terminal missingness is supported because the per-period `present = (N_mat[:, t] > 0) & (N_mat[:, t-1] > 0)` guard appears at three sites in the variance computation (`_compute_per_period_dids`, `_compute_full_per_group_contributions`, `_compute_cohort_recentered_inputs`) and cleanly masks out missing transitions without propagating NaN into the arithmetic. **Workaround for unbalanced panels:** pre-process your data to back-fill the baseline (or drop late-entry groups before fitting), or use R `DIDmultiplegtDYN` until a future phase lifts the restriction. The Step 5b `ValueError` and `UserWarning` messages name the offending group IDs so you can locate them quickly. + +**Reference implementation(s):** +- R: [`DIDmultiplegtDYN`](https://cran.r-project.org/package=DIDmultiplegtDYN) (CRAN, maintained by the paper authors). The Python implementation matches `did_multiplegt_dyn(..., effects=1)` at horizon `l = 1`. Parity tests live in `tests/test_chaisemartin_dhaultfoeuille_parity.py`. +- Stata: `did_multiplegt_dyn` (SSC, also maintained by the paper authors). + +**Requirements checklist:** +- [x] Single class `ChaisemartinDHaultfoeuille` (alias `DCDH`); not a family +- [x] Forward-compat `fit()` signature with `NotImplementedError` gates for Phase 2/3 parameters (`aggregate`, `L_max`, `controls`, `trends_linear`, `trends_nonparam`, `honest_did`, `survey_design`) +- [x] `DID_M` point estimate with cohort-recentered analytical SE +- [x] Joiners-only `DID_+` and leavers-only `DID_-` decompositions with their own inference +- [x] Single-lag placebo `DID_M^pl` (point estimate; SE deferred to Phase 2) +- [x] TWFE decomposition diagnostic (Theorem 1 of AER 2020): per-cell weights, fraction negative, `sigma_fe`, `beta_fe` +- [x] Standalone `twowayfeweights()` helper for users who only want the TWFE diagnostic +- [x] Multiplier bootstrap with Rademacher / Mammen / Webb weights, clustered at group +- [x] `drop_larger_lower=True` default (matches R `DIDmultiplegtDYN`); `False` opt-in with explicit inconsistency warning +- [x] Singleton-baseline filter (footnote 15 of dynamic paper, variance computation only) with explicit warning +- [x] Never-switching groups participate in the variance via stable-control roles after the Round 2 full-IF fix; `n_groups_dropped_never_switching` field retained as backwards-compatibility metadata only +- [x] Balanced-baseline panel requirement: missing-baseline groups raise `ValueError`; interior-gap groups dropped with `UserWarning`; terminal missingness retained (deviation from R `DIDmultiplegtDYN` documented as a Note) +- [x] A11 zero-retention convention with per-period boolean flags (`did_plus_t_a11_zeroed` / `did_minus_t_a11_zeroed`) and consolidated warning +- [x] No silent failures: every drop / round / fallback emits a `warnings.warn()` or `ValueError` +- [x] Hand-calculable 4-group worked example: `DID_M = 2.5`, `DID_+ = 2.0`, `DID_- = 3.0` exactly +- [x] R `DIDmultiplegtDYN` parity tests at `l = 1` (fixture skips cleanly when R or `DIDmultiplegtDYN` is unavailable) + +--- + ## ContinuousDiD **Primary Source:** Callaway, Goodman-Bacon & Sant'Anna (2024), "Difference-in-Differences with a Continuous Treatment," NBER Working Paper 32117. diff --git a/docs/practitioner_decision_tree.rst b/docs/practitioner_decision_tree.rst index 5ff0d2f32..3526ed25d 100644 --- a/docs/practitioner_decision_tree.rst +++ b/docs/practitioner_decision_tree.rst @@ -24,17 +24,22 @@ Which of these best describes your situation? Different markets started at different times. Go to :ref:`section-staggered`. -3. **I varied spending levels across markets** (e.g., $50K, $100K, $200K) +3. **My campaign turned on and off** (always-on with periodic dark periods, seasonal flights, holdout pulses) + + Treatment switches on AND off in the same market over time. Go to + :ref:`section-reversible`. + +4. **I varied spending levels across markets** (e.g., $50K, $100K, $200K) You want to know how the effect changes with the amount spent. Go to :ref:`section-dose`. -4. **I only have 3-5 test markets** +5. **I only have 3-5 test markets** Too few treated units for standard methods. Go to :ref:`section-few-markets`. -5. **I have survey data** (brand tracking, customer satisfaction, etc.) +6. **I have survey data** (brand tracking, customer satisfaction, etc.) Your outcome comes from a survey with complex sampling. Go to :ref:`section-survey`. @@ -130,6 +135,77 @@ a few months later, and so on. Different markets were treated at different times them into a single number. +.. _section-reversible: + +Reversible Treatment (On/Off Cycles) +------------------------------------ + +**Your situation:** Your campaign isn't a one-time launch. It runs in some markets, +then pauses for a few weeks, then resumes. Or you have always-on activity with +periodic "dark periods" where you go quiet in some markets to measure incrementality. +Or you run seasonal flights that go on, off, and back on across the year. + +The key feature: **the same market goes from treated to untreated to treated again**. +This breaks every other modern staggered estimator (Callaway-Sant'Anna, Sun-Abraham, +Imputation DiD, Two-Stage DiD, Efficient DiD, ETWFE), which all assume that once a +market is treated it stays treated. + +**Recommended method:** :class:`~diff_diff.ChaisemartinDHaultfoeuille` (alias :class:`~diff_diff.DCDH`) + +This is the **only library estimator** that handles non-absorbing (reversible) +treatments. It compares period-to-period outcome changes in markets that switch +into treatment ("joiners") and markets that switch out ("leavers"), against +simultaneously-stable controls. You get three numbers: the overall lift `DID_M`, +a joiners-only view `DID_+`, and a leavers-only view `DID_-`. + +.. code-block:: python + + from diff_diff import ChaisemartinDHaultfoeuille + from diff_diff.prep import generate_reversible_did_data + + # 80 markets, 6 periods, treatment switches on or off once per market + data = generate_reversible_did_data( + n_groups=80, n_periods=6, pattern="single_switch", seed=42, + ) + + est = ChaisemartinDHaultfoeuille() + results = est.fit( + data, outcome="outcome", group="group", + time="period", treatment="treatment", + ) + results.print_summary() + + print(f"Overall lift (DID_M): {results.overall_att:.2f}") + print(f"Joiners only (DID_+): {results.joiners_att:.2f}") + print(f"Leavers only (DID_-): {results.leavers_att:.2f}") + +.. note:: + + **Academic term:** This is the de Chaisemartin & D'Haultfœuille (2020) `DID_M` + estimator, equivalently `DID_1` (horizon `l = 1`) of their dynamic companion + paper (NBER WP 29873). It is the standard method for *non-absorbing* or + *reversible* treatments. The Python implementation matches the R + `DIDmultiplegtDYN` reference package maintained by the paper authors. + +.. warning:: + + By default, the estimator drops markets whose treatment switches more than + once before estimation (``drop_larger_lower=True``, matching the R reference). + Each drop emits a warning. If your design has many multi-switch markets and + you need them all, raise this with the diff-diff maintainers — Phase 2 of the + estimator will add explicit multi-switch handling via the dynamic event-study + path. + +.. note:: + + Single-lag placebo (`DID_M^pl`) is computed automatically and exposed via + ``results.placebo_effect``. The placebo inference fields (SE, p-value, CI) + are intentionally ``NaN`` in Phase 1 — and stay ``NaN`` even when + ``n_bootstrap > 0``. The dynamic companion paper Section 3.7.3 derives + the cohort-recentered analytical variance for ``DID_l`` only; + placebo-bootstrap support is deferred to Phase 2. + + .. _section-dose: Varying Spending Levels @@ -282,6 +358,9 @@ At a Glance * - Staggered rollout (waves) - ``CallawaySantAnna`` - Handles different launch dates correctly + * - On/off cycles (reversible treatment) + - ``ChaisemartinDHaultfoeuille`` + - Only library option for non-absorbing treatments * - Varied spending levels - ``ContinuousDiD`` - Dose-response curve @@ -296,10 +375,10 @@ At a Glance What About the Other Estimators? -------------------------------- -diff-diff has 16 estimators covering advanced scenarios: Sun-Abraham for +diff-diff has 17 estimators covering advanced scenarios: Sun-Abraham for interaction-weighted estimation, Imputation DiD and Two-Stage DiD for alternative staggered approaches, Stacked DiD, Efficient DiD, Triple Difference, TROP, and more. -The five scenarios above cover the most common business use cases. +The six scenarios above cover the most common business use cases. For the full academic decision tree with all estimators, see :doc:`choosing_estimator`. diff --git a/tests/conftest.py b/tests/conftest.py index 6f377ceb4..32bbfe754 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -92,6 +92,92 @@ def test_comparison_with_r(require_r): pytest.skip("R or did package not available") +# ============================================================================= +# R Availability Fixtures: DIDmultiplegtDYN (dCDH) +# ============================================================================= +# +# Parallel cache + fixture for the de Chaisemartin-D'Haultfoeuille (dCDH) +# parity tests, which require the R package `DIDmultiplegtDYN` (CRAN v2.3.3+) +# rather than `did`. The fixture is independent so missing `DIDmultiplegtDYN` +# does not affect CallawaySantAnna parity tests, and vice versa. + +_r_dcdh_available_cache = None + + +def _check_r_dcdh_available() -> bool: + """ + Check if R and the DIDmultiplegtDYN package are available (cached). + + This is called lazily when the r_dcdh_available fixture is first used, + not at module import time, to avoid subprocess latency during test + collection. + + Honors the `DIFF_DIFF_R=skip` environment variable, matching the + existing `_check_r_available` pattern, so users can force-skip all + R-dependent tests in one place. + + Returns + ------- + bool + True if R and DIDmultiplegtDYN are available, False otherwise. + """ + global _r_dcdh_available_cache + if _r_dcdh_available_cache is None: + r_env = os.environ.get("DIFF_DIFF_R", "auto").lower() + if r_env == "skip": + _r_dcdh_available_cache = False + else: + try: + result = subprocess.run( + [ + "Rscript", + "-e", + "library(DIDmultiplegtDYN); library(jsonlite); cat('OK')", + ], + capture_output=True, + text=True, + timeout=30, + ) + _r_dcdh_available_cache = result.returncode == 0 and "OK" in result.stdout + except (subprocess.TimeoutExpired, FileNotFoundError, OSError): + _r_dcdh_available_cache = False + return _r_dcdh_available_cache + + +@pytest.fixture(scope="session") +def r_dcdh_available(): + """ + Lazy check for R + DIDmultiplegtDYN availability. + + Session-scoped and cached. Independent of `r_available` so a missing + `DIDmultiplegtDYN` does not affect tests that depend only on `did`. + + Returns + ------- + bool + True if R and DIDmultiplegtDYN are available. + """ + return _check_r_dcdh_available() + + +@pytest.fixture +def require_r_dcdh(r_dcdh_available): + """ + Skip test if R + DIDmultiplegtDYN is not available. + + Use this fixture in dCDH (de Chaisemartin-D'Haultfoeuille) parity + tests that compare Python output to R `did_multiplegt_dyn`: + + ```python + def test_dcdh_parity_with_r(require_r_dcdh): + # Skipped when R or DIDmultiplegtDYN missing + ... + ``` + """ + if not r_dcdh_available: + pytest.skip("R or DIDmultiplegtDYN package not available") + + # ============================================================================= # CI Performance: Backend-Aware Parameter Scaling # ============================================================================= @@ -99,8 +185,7 @@ def test_comparison_with_r(require_r): from diff_diff._backend import HAS_RUST_BACKEND _PURE_PYTHON_MODE = ( - os.environ.get("DIFF_DIFF_BACKEND", "auto").lower() == "python" - or not HAS_RUST_BACKEND + os.environ.get("DIFF_DIFF_BACKEND", "auto").lower() == "python" or not HAS_RUST_BACKEND ) @@ -168,16 +253,12 @@ def assert_nan_inference(inference_dict): f"assert_nan_inference called but SE={se} is finite and positive. " "This helper is for validating NaN propagation when SE is invalid." ) - assert np.isnan(inference_dict["t_stat"]), ( - f"t_stat should be NaN when SE={se}, got {inference_dict['t_stat']}" - ) - assert np.isnan(inference_dict["p_value"]), ( - f"p_value should be NaN when SE={se}, got {inference_dict['p_value']}" - ) + assert np.isnan( + inference_dict["t_stat"] + ), f"t_stat should be NaN when SE={se}, got {inference_dict['t_stat']}" + assert np.isnan( + inference_dict["p_value"] + ), f"p_value should be NaN when SE={se}, got {inference_dict['p_value']}" ci = inference_dict["conf_int"] - assert np.isnan(ci[0]), ( - f"ci_lower should be NaN when SE={se}, got {ci[0]}" - ) - assert np.isnan(ci[1]), ( - f"ci_upper should be NaN when SE={se}, got {ci[1]}" - ) + assert np.isnan(ci[0]), f"ci_lower should be NaN when SE={se}, got {ci[0]}" + assert np.isnan(ci[1]), f"ci_upper should be NaN when SE={se}, got {ci[1]}" diff --git a/tests/test_chaisemartin_dhaultfoeuille.py b/tests/test_chaisemartin_dhaultfoeuille.py new file mode 100644 index 000000000..1acce2d2a --- /dev/null +++ b/tests/test_chaisemartin_dhaultfoeuille.py @@ -0,0 +1,1731 @@ +""" +API and behavior tests for ``ChaisemartinDHaultfoeuille`` (dCDH) — Phase 1. + +Covers basic API, validation, forward-compat NotImplementedError gates, +``drop_larger_lower``, A11 zero-retention, NaN handling, bootstrap +plumbing, and the results dataclass round-trip. Methodology validation +(hand-calculable arithmetic, cohort recentering correctness, parity +against R) lives in ``test_methodology_chaisemartin_dhaultfoeuille.py``. +""" + +import warnings + +import numpy as np +import pandas as pd +import pytest + +from diff_diff import ( + DCDH, + ChaisemartinDHaultfoeuille, + ChaisemartinDHaultfoeuilleResults, + DCDHBootstrapResults, + chaisemartin_dhaultfoeuille, + twowayfeweights, +) +from diff_diff.prep import generate_reversible_did_data + +# ============================================================================= +# Basic API +# ============================================================================= + + +class TestChaisemartinDHaultfoeuilleBasicAPI: + """Smoke tests for the basic happy path.""" + + def test_fit_returns_results_object(self): + data = generate_reversible_did_data(n_groups=40, n_periods=5, seed=1) + est = ChaisemartinDHaultfoeuille() + results = est.fit( + data, + outcome="outcome", + group="group", + time="period", + treatment="treatment", + ) + assert isinstance(results, ChaisemartinDHaultfoeuilleResults) + assert est.is_fitted_ is True + assert est.results_ is results + + def test_fit_recovers_homogeneous_effect_single_switch(self): + # With seed and n=120, the analytical CI should bracket the truth + data = generate_reversible_did_data( + n_groups=120, + n_periods=6, + treatment_effect=2.0, + seed=42, + ) + est = ChaisemartinDHaultfoeuille() + results = est.fit( + data, + outcome="outcome", + group="group", + time="period", + treatment="treatment", + ) + # CI should bracket the true effect of 2.0 + lo, hi = results.overall_conf_int + assert lo <= 2.0 <= hi, f"95% CI [{lo:.3f}, {hi:.3f}] does not bracket true effect 2.0" + + def test_fit_with_joiners_only_pattern(self): + # Use n_periods=10 so the random switch times don't saturate the + # final period (which would zero the last period via A11 and bias + # DID_M toward zero). 10 periods + 80 groups + uniform switch times + # leaves enough late-period stable_0 controls. + data = generate_reversible_did_data( + n_groups=80, + n_periods=10, + pattern="joiners_only", + treatment_effect=1.5, + seed=2, + ) + est = ChaisemartinDHaultfoeuille() + results = est.fit( + data, + outcome="outcome", + group="group", + time="period", + treatment="treatment", + ) + # Joiners present, no leavers + assert results.joiners_available is True + assert results.leavers_available is False + assert np.isnan(results.leavers_att) + # CI brackets the truth (modulo conservative-CI noise) + lo, hi = results.overall_conf_int + assert lo <= 1.5 <= hi, ( + f"95% CI [{lo:.3f}, {hi:.3f}] does not bracket true effect 1.5; " + f"DID_M = {results.overall_att:.3f}" + ) + + def test_fit_with_leavers_only_pattern(self): + # Same n_periods rationale as the joiners_only test + data = generate_reversible_did_data( + n_groups=80, + n_periods=10, + pattern="leavers_only", + treatment_effect=1.5, + seed=3, + ) + est = ChaisemartinDHaultfoeuille() + results = est.fit( + data, + outcome="outcome", + group="group", + time="period", + treatment="treatment", + ) + assert results.joiners_available is False + assert results.leavers_available is True + assert np.isnan(results.joiners_att) + + def test_missing_column_raises_value_error(self): + data = generate_reversible_did_data(n_groups=20, n_periods=4, seed=1) + est = ChaisemartinDHaultfoeuille() + with pytest.raises(ValueError, match="Missing columns"): + est.fit( + data, + outcome="bogus", + group="group", + time="period", + treatment="treatment", + ) + + def test_non_binary_treatment_raises_value_error(self): + df = pd.DataFrame( + { + "group": [1, 1, 2, 2], + "period": [0, 1, 0, 1], + "outcome": [10.0, 11.0, 10.0, 12.0], + "treatment": [0, 2, 0, 1], # 2 is non-binary + } + ) + est = ChaisemartinDHaultfoeuille() + with pytest.raises(ValueError, match="binary treatment"): + est.fit( + df, + outcome="outcome", + group="group", + time="period", + treatment="treatment", + ) + + def test_alias_DCDH_identity(self): + assert DCDH is ChaisemartinDHaultfoeuille + + def test_get_set_params(self): + est = ChaisemartinDHaultfoeuille(alpha=0.10, n_bootstrap=99, seed=7) + params = est.get_params() + assert params["alpha"] == 0.10 + assert params["n_bootstrap"] == 99 + assert params["seed"] == 7 + assert "drop_larger_lower" in params + assert "twfe_diagnostic" in params + assert "placebo" in params + + est.set_params(alpha=0.01, drop_larger_lower=False) + assert est.alpha == 0.01 + assert est.drop_larger_lower is False + + def test_set_params_unknown_raises(self): + est = ChaisemartinDHaultfoeuille() + with pytest.raises(ValueError, match="Unknown parameter"): + est.set_params(bogus_param=True) + + def test_convenience_function_matches_class(self): + data = generate_reversible_did_data(n_groups=40, n_periods=5, seed=1) + results_class = ChaisemartinDHaultfoeuille(seed=1).fit( + data, + outcome="outcome", + group="group", + time="period", + treatment="treatment", + ) + results_fn = chaisemartin_dhaultfoeuille( + data, + outcome="outcome", + group="group", + time="period", + treatment="treatment", + seed=1, + ) + # Same point estimate + assert results_class.overall_att == pytest.approx(results_fn.overall_att) + assert results_class.overall_se == pytest.approx(results_fn.overall_se) + + def test_minimal_computation_path(self): + # Disable everything optional; verify still works + data = generate_reversible_did_data(n_groups=30, n_periods=4, seed=1) + est = ChaisemartinDHaultfoeuille( + twfe_diagnostic=False, + placebo=False, + n_bootstrap=0, + ) + results = est.fit( + data, + outcome="outcome", + group="group", + time="period", + treatment="treatment", + ) + # TWFE fields should be None + assert results.twfe_weights is None + assert results.twfe_beta_fe is None + # Placebo should be NaN with available=False + assert results.placebo_available is False + assert np.isnan(results.placebo_effect) + # Bootstrap should be None + assert results.bootstrap_results is None + # Main estimate should still be finite + assert np.isfinite(results.overall_att) + + +# ============================================================================= +# Forward-compat NotImplementedError gates +# ============================================================================= + + +class TestForwardCompatGates: + """Each Phase 2/3/deferred parameter must raise NotImplementedError.""" + + @pytest.fixture + def data(self): + return generate_reversible_did_data(n_groups=20, n_periods=4, seed=1) + + def _est(self): + return ChaisemartinDHaultfoeuille() + + def test_aggregate_simple_raises_not_implemented(self, data): + # Per MEDIUM #1: even "simple" must be rejected; require aggregate=None exactly + with pytest.raises(NotImplementedError, match="Phase 2"): + self._est().fit( + data, + outcome="outcome", + group="group", + time="period", + treatment="treatment", + aggregate="simple", + ) + + def test_aggregate_event_study_raises_not_implemented(self, data): + with pytest.raises(NotImplementedError, match="Phase 2"): + self._est().fit( + data, + outcome="outcome", + group="group", + time="period", + treatment="treatment", + aggregate="event_study", + ) + + def test_L_max_raises_not_implemented(self, data): + with pytest.raises(NotImplementedError, match="Phase 2"): + self._est().fit( + data, + outcome="outcome", + group="group", + time="period", + treatment="treatment", + L_max=4, + ) + + def test_controls_raises_not_implemented(self, data): + with pytest.raises(NotImplementedError, match="Phase 3"): + self._est().fit( + data, + outcome="outcome", + group="group", + time="period", + treatment="treatment", + controls=["x"], + ) + + def test_trends_linear_raises_not_implemented(self, data): + with pytest.raises(NotImplementedError, match="Phase 3"): + self._est().fit( + data, + outcome="outcome", + group="group", + time="period", + treatment="treatment", + trends_linear=True, + ) + + def test_trends_nonparam_raises_not_implemented(self, data): + with pytest.raises(NotImplementedError, match="Phase 3"): + self._est().fit( + data, + outcome="outcome", + group="group", + time="period", + treatment="treatment", + trends_nonparam="state", + ) + + def test_honest_did_raises_not_implemented(self, data): + with pytest.raises(NotImplementedError, match="Phase 3"): + self._est().fit( + data, + outcome="outcome", + group="group", + time="period", + treatment="treatment", + honest_did=True, + ) + + def test_survey_design_raises_not_implemented(self, data): + with pytest.raises(NotImplementedError, match="separate effort"): + self._est().fit( + data, + outcome="outcome", + group="group", + time="period", + treatment="treatment", + survey_design=object(), + ) + + def test_cluster_parameter_raises_not_implemented(self, data): + """ + Per Phase 1 cluster contract: dCDH always clusters at the + group level via the cohort-recentered influence function + (analytical SEs) and the multiplier bootstrap (also grouped at + the group column). Custom clustering is not supported in + Phase 1. + + The reviewer flagged that ``cluster`` was previously accepted + on ``__init__`` and stored on ``self.cluster`` but never + actually read by ``fit()`` or ``_compute_dcdh_bootstrap()``, + making it a silent no-op. This test pins the new contract: any + non-None cluster value raises ``NotImplementedError`` at + construction time with a message naming the offending value + and pointing at the Phase 1 reservation. The same gate fires + from ``set_params``. + + See REGISTRY.md ``Note (Phase 1 cluster contract)``. + """ + # __init__ rejects any non-None cluster + with pytest.raises(NotImplementedError, match=r"cluster.*Phase 1"): + ChaisemartinDHaultfoeuille(cluster="state") + with pytest.raises(NotImplementedError, match=r"cluster.*Phase 1"): + ChaisemartinDHaultfoeuille(cluster="unit") + + # set_params after construction also rejects + est = ChaisemartinDHaultfoeuille() + with pytest.raises(NotImplementedError, match=r"cluster.*Phase 1"): + est.set_params(cluster="state") + + # cluster=None still works (the only supported value) + est_default = ChaisemartinDHaultfoeuille(cluster=None) + assert est_default.cluster is None + assert est_default.get_params()["cluster"] is None + + # The convenience function also rejects (forward-compat gate + # propagates through the wrapper at __init__ time) + with pytest.raises(NotImplementedError, match=r"cluster.*Phase 1"): + chaisemartin_dhaultfoeuille( + data, + outcome="outcome", + group="group", + time="period", + treatment="treatment", + cluster="state", + ) + + def test_rank_deficient_action_error_raises_on_fitted_twfe(self): + """ + The TWFE diagnostic requires at least 2 groups and 2 periods + to build a meaningful FE design. A 1-group panel triggers a + ValueError from _build_group_time_design's guard, and when + rank_deficient_action="error" the blanket except in fit() + re-raises it instead of swallowing it as a warning. + + This also exercises the code path where rank_deficient_action + ="warn" downgrades the failure to a warning so the main + estimation can proceed. + """ + # 1 group, 2 periods: triggers "at least 2 groups" guard + df = pd.DataFrame( + { + "group": [1, 1], + "period": [0, 1], + "treatment": [0, 1], + "outcome": [10.0, 12.0], + } + ) + # rank_deficient_action="error" should propagate through + est = ChaisemartinDHaultfoeuille(twfe_diagnostic=True, rank_deficient_action="error") + with pytest.raises(ValueError, match="at least 2 groups"): + est.fit( + df, + outcome="outcome", + group="group", + time="period", + treatment="treatment", + ) + + # rank_deficient_action="warn" should NOT raise the TWFE error + est_warn = ChaisemartinDHaultfoeuille(twfe_diagnostic=True, rank_deficient_action="warn") + with warnings.catch_warnings(record=True): + warnings.simplefilter("always") + try: + est_warn.fit( + df, + outcome="outcome", + group="group", + time="period", + treatment="treatment", + ) + except ValueError as exc: + # Acceptable if the error is from main estimation + # (not from the TWFE diagnostic guard) + assert "at least 2 groups" not in str(exc) + + +# ============================================================================= +# drop_larger_lower (Critical #1) +# ============================================================================= + + +class TestDropLargerLower: + """Multi-switch group filtering matches R DIDmultiplegtDYN behavior.""" + + def test_default_drops_a5_violators_with_warning(self): + # Mix of single-switch groups and one explicit multi-switch group + data = generate_reversible_did_data( + n_groups=40, + n_periods=4, + pattern="single_switch", + seed=1, + ) + # Inject a multi-switch group: switch 0 -> 1 -> 0 + multi_switch = pd.DataFrame( + { + "group": [9999] * 4, + "period": [0, 1, 2, 3], + "treatment": [0, 1, 1, 0], + "outcome": [10.0, 13.0, 14.0, 11.0], + "true_effect": [0.0, 2.0, 2.0, 0.0], + "d_lag": [np.nan, 0.0, 1.0, 1.0], + "switcher_type": ["initial", "joiner", "stable_1", "leaver"], + } + ) + data = pd.concat([data, multi_switch], ignore_index=True) + + est = ChaisemartinDHaultfoeuille() + with warnings.catch_warnings(record=True) as w: + warnings.simplefilter("always") + results = est.fit( + data, + outcome="outcome", + group="group", + time="period", + treatment="treatment", + ) + # The multi-switch group should be dropped + assert results.n_groups_dropped_crossers >= 1 + assert 9999 not in results.groups + # A drop_larger_lower warning should fire + assert any("drop_larger_lower" in str(wi.message) for wi in w) + + def test_drop_larger_lower_false_emits_inconsistency_warning(self): + data = generate_reversible_did_data( + n_groups=40, + n_periods=4, + pattern="single_switch", + seed=1, + ) + est = ChaisemartinDHaultfoeuille(drop_larger_lower=False) + with warnings.catch_warnings(record=True) as w: + warnings.simplefilter("always") + est.fit( + data, + outcome="outcome", + group="group", + time="period", + treatment="treatment", + ) + # Inconsistency warning should fire + assert any("drop_larger_lower=False" in str(wi.message) for wi in w) + + def test_drop_larger_lower_true_no_op_on_single_switch_data(self): + data = generate_reversible_did_data( + n_groups=40, + n_periods=5, + pattern="single_switch", + seed=1, + ) + est = ChaisemartinDHaultfoeuille() + results = est.fit( + data, + outcome="outcome", + group="group", + time="period", + treatment="treatment", + ) + assert results.n_groups_dropped_crossers == 0 + + def test_singleton_baseline_filter_variance_only(self): + # Build a panel where one group has a unique baseline (e.g., only group + # with D_{g,0}=1). This is the footnote-15 condition. + # + # Per the variance-only filter (the dCDH Round 2 fix), the singleton- + # baseline group is identified, counted in + # n_groups_dropped_singleton_baseline, and excluded from the cohort- + # recentered VARIANCE. But it remains in the point-estimate sample + # as a period-based stable control (matching Python's documented + # period-vs-cohort stable-control interpretation). + data = generate_reversible_did_data( + n_groups=20, + n_periods=4, + pattern="joiners_only", + seed=1, + ) + # Inject a single leaver group (unique baseline=1) + leaver = pd.DataFrame( + { + "group": [9999] * 4, + "period": [0, 1, 2, 3], + "treatment": [1, 0, 0, 0], + "outcome": [10.0, 9.0, 8.0, 7.0], + "true_effect": [0.0, 0.0, 0.0, 0.0], + "d_lag": [np.nan, 1.0, 0.0, 0.0], + "switcher_type": ["initial", "leaver", "stable_0", "stable_0"], + } + ) + data = pd.concat([data, leaver], ignore_index=True) + + est = ChaisemartinDHaultfoeuille() + with warnings.catch_warnings(record=True) as w: + warnings.simplefilter("always") + results = est.fit( + data, + outcome="outcome", + group="group", + time="period", + treatment="treatment", + ) + # The leaver has a unique baseline (D=1) -> excluded from variance. + assert results.n_groups_dropped_singleton_baseline >= 1 + # Per the variance-only filter, the group is RETAINED in the + # point-estimate sample (it can serve as a period-based stable + # control), so it appears in results.groups. + assert 9999 in results.groups + # The warning text mentions the variance-only scope. + assert any("Singleton-baseline" in str(wi.message) for wi in w) + assert any( + "VARIANCE computation only" in str(wi.message) for wi in w + ), "Warning text should clarify the filter is variance-only" + + def test_missing_baseline_period_raises_value_error(self): + """ + Per fit() Step 5b: groups missing the first global period have + an undefined baseline D_{g,1} and must be rejected with a clear + error rather than crashing the cohort enumeration with NaN. + """ + data = generate_reversible_did_data(n_groups=10, n_periods=5, seed=1) + # Drop period 0 for group 5 (a "late-entry" group) + data = data[~((data["group"] == 5) & (data["period"] == 0))].reset_index(drop=True) + est = ChaisemartinDHaultfoeuille() + with pytest.raises(ValueError, match="missing this baseline"): + est.fit( + data, + outcome="outcome", + group="group", + time="period", + treatment="treatment", + ) + + def test_interior_gap_drops_group_with_warning(self): + """ + Per fit() Step 5b: groups with missing intermediate periods + (interior gaps between their first and last observed period) + are dropped with an explicit warning. The cohort/variance path + requires consecutive observed periods to detect first switches + unambiguously. + """ + data = generate_reversible_did_data(n_groups=10, n_periods=5, seed=1) + # Drop period 2 for group 3 (interior gap: g=3 has periods 0, 1, 3, 4) + data = data[~((data["group"] == 3) & (data["period"] == 2))].reset_index(drop=True) + est = ChaisemartinDHaultfoeuille() + with warnings.catch_warnings(record=True) as w: + warnings.simplefilter("always") + results = est.fit( + data, + outcome="outcome", + group="group", + time="period", + treatment="treatment", + ) + # Group 3 was dropped from the post-filter sample + assert 3 not in results.groups + # The interior-gap warning fired + assert any("interior period gaps" in str(wi.message) for wi in w) + # Other groups still present + assert len(results.groups) == 9 + + def test_terminal_missingness_retained(self): + """ + Per fit() Step 5b contract: groups observed at the baseline but + missing one or more LATER periods (terminal missingness / early + exit / right-censoring) are RETAINED. The group contributes from + its observed periods only, masked out of missing transitions by + the per-period ``present = (N_mat[:, t] > 0) & (N_mat[:, t-1] > 0)`` + guard at three sites in the variance computation + (``_compute_per_period_dids``, ``_compute_full_per_group_contributions``, + ``_compute_cohort_recentered_inputs``). NaN never propagates into + the arithmetic because ``D_mat[g, t]`` and ``Y_mat[g, t]`` are + never read without first checking ``N_mat[g, t] > 0``. + + This pins the remaining unspoken branch of the ragged-panel + contract that fit() validates: missing baseline -> ValueError; + interior gap -> drop with warning; terminal missingness -> retained. + See REGISTRY.md ``Note (deviation from R DIDmultiplegtDYN)`` for + the documented contract and the rationale for supporting only + terminal missingness in Phase 1. + """ + data = generate_reversible_did_data(n_groups=10, n_periods=5, seed=1) + # Group 5 has periods 0, 1, 2 only (terminal missingness: missing 3, 4) + data = data[~((data["group"] == 5) & (data["period"].isin([3, 4])))].reset_index(drop=True) + est = ChaisemartinDHaultfoeuille() + # The fit completes without error + results = est.fit( + data, + outcome="outcome", + group="group", + time="period", + treatment="treatment", + ) + # Group 5 is RETAINED in the post-filter sample (NOT dropped) + assert 5 in results.groups + # All 10 groups remain + assert len(results.groups) == 10 + # The point estimate is well-defined (not NaN) + assert np.isfinite(results.overall_att) + # Per-period DIDs were computed (the structure of per_period_effects + # depends on the panel's switch pattern; assert at least one entry + # was populated rather than asserting specific counts) + assert len(results.per_period_effects) > 0 + + def test_global_period_gap_treated_as_adjacent(self): + """ + Per the REGISTRY.md period-index semantics contract: the + estimator operates on sorted period indices, not calendar dates. + A panel with periods [0, 1, 3] (period 2 missing for ALL groups) + is treated as a valid 3-period panel where period 3 is the + immediate successor of period 1. No error, no warning, no + imputation. This is consistent with the AER 2020 paper's + Theorem 3 (adjacent sorted periods) and R DIDmultiplegtDYN. + + This test pins the contract so a future change doesn't + accidentally start rejecting or warning on globally missing + calendar periods. + """ + # 4 groups × 3 periods [0, 1, 3] — all groups present at all + # three periods, no interior gaps, just a global calendar gap + df = pd.DataFrame( + { + "group": [1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4], + "period": [0, 1, 3, 0, 1, 3, 0, 1, 3, 0, 1, 3], + "treatment": [0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1], + "outcome": [ + 10, + 11, + 15, + 10, + 11, + 14, + 10, + 11, + 12, + 12, + 13, + 14, + ], + } + ) + est = ChaisemartinDHaultfoeuille() + # The fit completes without error + results = est.fit( + df, + outcome="outcome", + group="group", + time="period", + treatment="treatment", + ) + # All 4 groups present + assert len(results.groups) == 4 + # Point estimate is finite + assert np.isfinite(results.overall_att) + # Per-period effects include the transition at t=3 (treated as + # the successor of t=1) + assert len(results.per_period_effects) > 0 + + def test_cell_count_weighting_unbalanced_input(self): + """ + Regression test: dCDH must use cell counts (paper-literal), + not within-cell observation counts, as the Theorem 3 N_{a,b,t} + weights. + + Constructed with two joiner groups whose (g, t) cells contain + very different numbers of original observations (group 1 has + 100 obs/cell, group 2 has 1 obs/cell). Both joiners have the + same true effect under the cell-weighted formula. + + Under cell weighting (paper-literal, the correct behavior), + each cell contributes equally and the result equals the simple + average of cell-level effects (~5.0). Under the bug behavior + (sample-size weighting), group 1 dominates by 100x because its + cells contribute 100x the weight. + + On a noiseless DGP both formulas would give 5.0; we add a + deliberate per-cell perturbation to group 1 so that the bug + would be visible: under sample-size weighting the result + would shift toward group 1's cell mean (which is perturbed), + while under cell weighting group 2's pristine effect would + anchor the average. + """ + records = [] + # Group 1: 100 obs per cell, joiner at t=2, but with a +0.5 + # perturbation to its post-treatment cell mean (so its cell + # effect is 5.5, not 5.0) + for t in [0, 1, 2]: + for i in range(100): + d = 1 if t == 2 else 0 + base = 10.0 + noise = 0.0 # noiseless within cell + if t == 2: + y = base + 5.5 + noise # perturbed post effect + else: + y = base + noise + records.append({"group": 1, "period": t, "treatment": d, "outcome": y}) + # Group 2: 1 obs per cell, joiner at t=2, clean effect of 5.0 + for t in [0, 1, 2]: + d = 1 if t == 2 else 0 + y = 10.0 + (5.0 if d == 1 else 0) + records.append({"group": 2, "period": t, "treatment": d, "outcome": y}) + # Stable controls + for g in [3, 4]: + for t in [0, 1, 2]: + records.append( + { + "group": g, + "period": t, + "treatment": 0, + "outcome": 10.0, + } + ) + + df = pd.DataFrame(records) + est = ChaisemartinDHaultfoeuille() + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + results = est.fit( + df, outcome="outcome", group="group", time="period", treatment="treatment" + ) + + # Expected under CELL weighting: + # DID_+,2 = avg over joiner cells - avg over stable_0 cells + # = avg(5.5, 5.0) - avg(0, 0) = 5.25 + # Expected under SAMPLE-SIZE weighting (the bug): + # DID_+,2 = (100*5.5 + 1*5.0) / 101 - 0 = 5.495 + # The two differ by ~0.25, so we can detect the bug at 0.05 tolerance. + assert abs(results.overall_att - 5.25) < 0.05, ( + f"Expected DID_M ≈ 5.25 under cell weighting, got " + f"{results.overall_att:.4f}. If you see ~5.495 the estimator " + f"is using sample-size weighting (the bug)." + ) + # n_switcher_cells should be 2 (one cell per joiner group at t=2), + # NOT 101 (the total observation count) + assert results.n_switcher_cells == 2, ( + f"n_switcher_cells should be 2 (cell count), got " + f"{results.n_switcher_cells}. If you see 101 the estimator " + f"is using sample-size weighting (the bug)." + ) + + +# ============================================================================= +# A11 zero-retention (Critical #2) +# ============================================================================= + + +class TestA11Handling: + """Assumption 11 violations are zeroed in numerator, retained in denominator.""" + + def test_a11_violation_zero_in_numerator_retain_in_denominator(self): + # 4-group, 3-period panel where at t=2 there are joiners (g=1, g=2) + # but no stable_0 controls. Both baselines (0, 1) are non-singleton + # (2 groups each), so the singleton-baseline filter is a no-op. + df = pd.DataFrame( + { + "group": [1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4], + "period": [0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2], + "treatment": [0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1], + "outcome": [10.0, 11.0, 14.0, 10.0, 11.0, 14.0, 10.0, 11.0, 12.0, 10.0, 11.0, 12.0], + } + ) + # At t=2: joiners = {g=1, g=2}; stable_1 = {g=3, g=4}; NO stable_0 -> A11 violated + est = ChaisemartinDHaultfoeuille() + with warnings.catch_warnings(record=True) as w: + warnings.simplefilter("always") + results = est.fit( + df, + outcome="outcome", + group="group", + time="period", + treatment="treatment", + ) + # A11 warning should fire + assert any("Assumption 11" in str(wi.message) for wi in w) + # Per-period decomposition: t=2 should be A11-zeroed for joiners + cell_t2 = results.per_period_effects[2] + assert cell_t2["did_plus_t"] == 0.0 + assert cell_t2["did_plus_t_a11_zeroed"] is True + # The joiner count is retained in N_S + assert cell_t2["n_10_t"] == 2 + + def test_placebo_a11_violation_emits_warning(self): + """ + Mirror of the main A11 contract for the placebo: + when placebo joiners exist (3-period stable D=0 history then + switch) but no group provides a 3-period stable_0 control, + the affected placebo period contribution is zeroed AND a + consolidated ``Placebo (DID_M^pl) Assumption 11 violations`` + warning fires from ``fit()``. + + Construct: 4-group T=3 panel with two D=[0,0,1] joiners (also + placebo joiners at t=2) and two always-treated controls. No + group has D=[0,0,0], so the placebo joiner side has no + stable_0 control. The main path also has an A11 violation + on the same panel (its own warning fires too); this test + asserts the PLACEBO warning specifically. + """ + df = pd.DataFrame( + { + "group": [1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4], + "period": [0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2], + "treatment": [0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1], + "outcome": [ + 10.0, + 11.0, + 15.0, + 10.0, + 11.0, + 16.0, + 12.0, + 13.0, + 14.0, + 12.0, + 13.0, + 14.0, + ], + } + ) + est = ChaisemartinDHaultfoeuille() + with warnings.catch_warnings(record=True) as w: + warnings.simplefilter("always") + results = est.fit( + df, + outcome="outcome", + group="group", + time="period", + treatment="treatment", + ) + # Placebo was computed (T >= 3 + qualifying cells) and is available + assert results.placebo_available + # The placebo A11 warning fired (text contains "Placebo" + "Assumption 11") + placebo_a11_warnings = [ + wi for wi in w if "Placebo" in str(wi.message) and "Assumption 11" in str(wi.message) + ] + assert len(placebo_a11_warnings) >= 1, ( + "Expected the placebo A11 warning to fire on a panel where placebo " + "joiners exist but no 3-period stable_0 controls exist. Got warnings: " + f"{[str(wi.message) for wi in w]}" + ) + # The warning should mention the affected placebo period + assert "stable_0" in str(placebo_a11_warnings[0].message) + + def test_a11_natural_zero_no_switchers_does_not_zero_flag(self): + data = generate_reversible_did_data( + n_groups=20, + n_periods=4, + pattern="joiners_only", + seed=1, + ) + est = ChaisemartinDHaultfoeuille() + results = est.fit( + data, + outcome="outcome", + group="group", + time="period", + treatment="treatment", + ) + # No leavers in joiners_only, so leaver A11 flag is always False + for t, cell in results.per_period_effects.items(): + if cell["n_01_t"] == 0: + assert cell["did_minus_t_a11_zeroed"] is False + + +# ============================================================================= +# NaN handling +# ============================================================================= + + +class TestNaNHandling: + def test_empty_dataframe_raises(self): + df = pd.DataFrame(columns=["group", "period", "treatment", "outcome"]) + est = ChaisemartinDHaultfoeuille() + with pytest.raises((ValueError, KeyError)): + est.fit( + df, + outcome="outcome", + group="group", + time="period", + treatment="treatment", + ) + + def test_no_switchers_raises(self): + # All groups stable -> dCDH cannot estimate. The exact error path + # depends on which filter fires first (singleton-baseline vs + # no-switching-cells), so accept either message. + df = pd.DataFrame( + { + "group": [1, 1, 1, 2, 2, 2], + "period": [0, 1, 2, 0, 1, 2], + "treatment": [0, 0, 0, 1, 1, 1], + "outcome": [10.0, 11.0, 12.0, 13.0, 14.0, 15.0], + } + ) + est = ChaisemartinDHaultfoeuille() + with pytest.raises(ValueError, match=r"(No switching cells|no groups remain)"): + est.fit( + df, + outcome="outcome", + group="group", + time="period", + treatment="treatment", + ) + + +# ============================================================================= +# Bootstrap inference +# ============================================================================= + + +class TestBootstrap: + @pytest.fixture + def data(self): + return generate_reversible_did_data(n_groups=80, n_periods=5, seed=1) + + def test_bootstrap_zero_uses_analytical(self, data): + est = ChaisemartinDHaultfoeuille(n_bootstrap=0) + results = est.fit( + data, + outcome="outcome", + group="group", + time="period", + treatment="treatment", + ) + assert results.bootstrap_results is None + assert np.isfinite(results.overall_se) + + def test_bootstrap_rademacher(self, data, ci_params): + n_boot = ci_params.bootstrap(199) + est = ChaisemartinDHaultfoeuille( + n_bootstrap=n_boot, + bootstrap_weights="rademacher", + seed=42, + ) + results = est.fit( + data, + outcome="outcome", + group="group", + time="period", + treatment="treatment", + ) + assert results.bootstrap_results is not None + assert isinstance(results.bootstrap_results, DCDHBootstrapResults) + assert results.bootstrap_results.n_bootstrap == n_boot + assert results.bootstrap_results.weight_type == "rademacher" + assert np.isfinite(results.bootstrap_results.overall_se) + assert results.bootstrap_results.overall_se > 0 + + def test_bootstrap_mammen(self, data, ci_params): + n_boot = ci_params.bootstrap(199) + est = ChaisemartinDHaultfoeuille( + n_bootstrap=n_boot, + bootstrap_weights="mammen", + seed=42, + ) + results = est.fit( + data, + outcome="outcome", + group="group", + time="period", + treatment="treatment", + ) + assert results.bootstrap_results is not None + assert results.bootstrap_results.weight_type == "mammen" + + def test_bootstrap_webb(self, data, ci_params): + n_boot = ci_params.bootstrap(199) + est = ChaisemartinDHaultfoeuille( + n_bootstrap=n_boot, + bootstrap_weights="webb", + seed=42, + ) + results = est.fit( + data, + outcome="outcome", + group="group", + time="period", + treatment="treatment", + ) + assert results.bootstrap_results is not None + assert results.bootstrap_results.weight_type == "webb" + + def test_placebo_bootstrap_unavailable_in_phase_1(self, data, ci_params): + """ + Phase 1 commitment: the placebo SE is intentionally NaN even when + ``n_bootstrap > 0``. The dynamic companion paper Section 3.7.3 + derives the cohort-recentered analytical variance for ``DID_l`` + only — the placebo's influence-function machinery is deferred to + Phase 2. The bootstrap path covers ``DID_M``, ``DID_+``, and + ``DID_-`` only. + + This test pins down the contract so that future contributors do + not silently widen the bootstrap surface to include the placebo + without also wiring up the documented Phase 2 derivation. If + Phase 2 implements the placebo bootstrap, this test should be + updated (not deleted) to assert finite placebo bootstrap fields. + """ + n_boot = ci_params.bootstrap(199) + est = ChaisemartinDHaultfoeuille( + n_bootstrap=n_boot, + placebo=True, + seed=42, + ) + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + results = est.fit( + data, + outcome="outcome", + group="group", + time="period", + treatment="treatment", + ) + + # Bootstrap is populated for the three implemented targets + assert results.bootstrap_results is not None + assert np.isfinite(results.bootstrap_results.overall_se) + + # Placebo bootstrap fields are explicitly None (not populated) + assert results.bootstrap_results.placebo_se is None + assert results.bootstrap_results.placebo_ci is None + assert results.bootstrap_results.placebo_p_value is None + + # Placebo inference fields on the main results stay NaN-consistent + assert np.isnan(results.placebo_se) + assert np.isnan(results.placebo_t_stat) + assert np.isnan(results.placebo_p_value) + assert np.isnan(results.placebo_conf_int[0]) + assert np.isnan(results.placebo_conf_int[1]) + + # The placebo point estimate itself is still computed and finite + # (the deferral is purely about inference, not the point estimate) + if results.placebo_available: + assert np.isfinite(results.placebo_effect) + + def test_bootstrap_p_value_and_ci_propagated_to_top_level(self, data, ci_params): + """ + Per the bootstrap inference surface contract: when + ``n_bootstrap > 0``, the top-level ``results.overall_*`` / + ``joiners_*`` / ``leavers_*`` p-value and CI fields hold the + percentile-based bootstrap inference computed by the + multiplier bootstrap, NOT normal-theory recomputations from + the bootstrap SE. The t-stat is still computed from the SE + (project anti-pattern rule: never compute t = effect/se + inline). + + Pre-Round-10, the dCDH ``fit()`` body silently called + ``safe_inference(overall_att, br.overall_se)`` and stored its + normal-theory p/CI on the top-level fields, which made the + public inference surface a hybrid (bootstrap SE + normal- + theory p/CI). Library precedent for the propagation: + ``imputation.py:790-805``, ``two_stage.py:778-787``, + ``efficient_did.py:1009-1013``. This test pins the new + contract. + + See REGISTRY.md ``ChaisemartinDHaultfoeuille`` ``Note + (bootstrap inference surface)``. + """ + n_boot = ci_params.bootstrap(199) + est = ChaisemartinDHaultfoeuille( + n_bootstrap=n_boot, + bootstrap_weights="rademacher", + seed=42, + ) + results = est.fit( + data, + outcome="outcome", + group="group", + time="period", + treatment="treatment", + ) + br = results.bootstrap_results + assert br is not None + + # Overall DID_M: top-level p-value and CI come from bootstrap + assert results.overall_p_value == pytest.approx(br.overall_p_value) + assert results.overall_conf_int == pytest.approx(br.overall_ci) + # The t-stat is computed from the SE (effect / se), not from + # a percentile distribution + assert np.isfinite(results.overall_t_stat) + expected_t = results.overall_att / results.overall_se + assert results.overall_t_stat == pytest.approx(expected_t) + + # Joiners + if results.joiners_available and br.joiners_p_value is not None: + assert results.joiners_p_value == pytest.approx(br.joiners_p_value) + assert results.joiners_conf_int == pytest.approx(br.joiners_ci) + + # Leavers + if results.leavers_available and br.leavers_p_value is not None: + assert results.leavers_p_value == pytest.approx(br.leavers_p_value) + assert results.leavers_conf_int == pytest.approx(br.leavers_ci) + + # event_study_effects[1] mirrors the top-level overall fields, + # so it should also reflect the bootstrap inference + assert results.event_study_effects is not None + assert 1 in results.event_study_effects + es = results.event_study_effects[1] + assert es["p_value"] == pytest.approx(br.overall_p_value) + assert es["conf_int"] == pytest.approx(br.overall_ci) + + # summary() and to_dataframe() chain off the top-level fields, + # so they automatically reflect the bootstrap inference. Smoke + # test that they don't crash and that the rendered values match + # the bootstrap output. + summary_text = results.summary() + assert "DID_M" in summary_text + # The summary footer should mention bootstrap inference, NOT + # the analytical-CI conservativeness note (which only applies + # when n_bootstrap=0). This pins the P2 fix from Round 11. + assert "multiplier-bootstrap percentile inference" in summary_text + assert "analytical CI is conservative" not in summary_text + df_overall = results.to_dataframe(level="overall") + assert df_overall.iloc[0]["p_value"] == pytest.approx(br.overall_p_value) + assert df_overall.iloc[0]["conf_int_lower"] == pytest.approx(br.overall_ci[0]) + assert df_overall.iloc[0]["conf_int_upper"] == pytest.approx(br.overall_ci[1]) + + def test_bootstrap_seed_reproducibility(self, data, ci_params): + n_boot = ci_params.bootstrap(99) + r1 = ChaisemartinDHaultfoeuille(n_bootstrap=n_boot, seed=42).fit( + data, + outcome="outcome", + group="group", + time="period", + treatment="treatment", + ) + r2 = ChaisemartinDHaultfoeuille(n_bootstrap=n_boot, seed=42).fit( + data, + outcome="outcome", + group="group", + time="period", + treatment="treatment", + ) + assert r1.overall_se == r2.overall_se + + +# ============================================================================= +# Results dataclass round-trip +# ============================================================================= + + +class TestResultsDataclass: + @pytest.fixture + def results(self): + data = generate_reversible_did_data(n_groups=40, n_periods=5, seed=1) + return ChaisemartinDHaultfoeuille().fit( + data, + outcome="outcome", + group="group", + time="period", + treatment="treatment", + ) + + def test_summary_formats_without_error(self, results): + out = results.summary() + assert isinstance(out, str) + assert "DID_M" in out + assert "DID_+" in out + assert "DID_-" in out + # Analytical mode (n_bootstrap=0) shows the conservative-CI note + assert "analytical CI is conservative" in out + assert "multiplier-bootstrap" not in out + + def test_print_summary(self, results, capsys): + results.print_summary() + captured = capsys.readouterr() + assert "DID_M" in captured.out + + def test_to_dataframe_overall(self, results): + df = results.to_dataframe("overall") + assert isinstance(df, pd.DataFrame) + assert len(df) == 1 + assert list(df.columns) == [ + "estimand", + "effect", + "se", + "t_stat", + "p_value", + "conf_int_lower", + "conf_int_upper", + ] + assert df.iloc[0]["estimand"] == "DID_M" + + def test_to_dataframe_joiners_leavers(self, results): + df = results.to_dataframe("joiners_leavers") + assert len(df) == 3 + assert set(df["estimand"].tolist()) == {"DID_M", "DID_+", "DID_-"} + # Round 4: n_cells and n_obs are separate columns with consistent + # units across all rows. n_cells counts switching (g, t) cells, + # n_obs sums raw observation counts over the same cells. The DID_M + # row uses the union of joiner + leaver cells. + assert "n_cells" in df.columns + assert "n_obs" in df.columns + # On balanced 1-obs-per-cell test data, n_cells == n_obs everywhere + for _, row in df.iterrows(): + assert row["n_cells"] == row["n_obs"], ( + f"On balanced data n_cells should equal n_obs for row " + f"{row['estimand']}, got n_cells={row['n_cells']}, " + f"n_obs={row['n_obs']}" + ) + # The DID_M row's count is the sum of the DID_+ and DID_- rows' + did_m_row = df[df["estimand"] == "DID_M"].iloc[0] + did_plus_row = df[df["estimand"] == "DID_+"].iloc[0] + did_minus_row = df[df["estimand"] == "DID_-"].iloc[0] + assert did_m_row["n_cells"] == did_plus_row["n_cells"] + did_minus_row["n_cells"] + + def test_to_dataframe_per_period(self, results): + df = results.to_dataframe("per_period") + assert isinstance(df, pd.DataFrame) + assert "period" in df.columns + assert "did_plus_t" in df.columns + assert "did_plus_t_a11_zeroed" in df.columns + + def test_to_dataframe_twfe_weights(self, results): + df = results.to_dataframe("twfe_weights") + assert isinstance(df, pd.DataFrame) + assert "weight" in df.columns + + def test_to_dataframe_unknown_level_raises(self, results): + with pytest.raises(ValueError, match="Unknown level"): + results.to_dataframe("bogus") + + def test_event_study_effects_populated_at_l1(self, results): + # Per review MEDIUM #5: in Phase 1, event_study_effects should not be + # None — it should hold a single key 1 with the same effect as overall_att + assert results.event_study_effects is not None + assert 1 in results.event_study_effects + es1 = results.event_study_effects[1] + assert es1["effect"] == pytest.approx(results.overall_att) + assert es1["se"] == pytest.approx(results.overall_se) + + def test_is_significant_property(self, results): + # Boolean reflects whether p-value < alpha + expected = results.overall_p_value < results.alpha + assert results.is_significant is expected + + def test_coef_var_nan_safe_on_non_finite_se(self): + # coef_var = SE / |ATT|. When SE is non-finite (NaN or Inf), the + # property must return NaN (not propagate the bad value). When SE + # is exactly 0, coef_var = 0 is correct (zero variance). + from diff_diff.chaisemartin_dhaultfoeuille_results import ( + ChaisemartinDHaultfoeuilleResults, + ) + + r_nan = ChaisemartinDHaultfoeuilleResults( + overall_att=2.0, + overall_se=float("nan"), + overall_t_stat=float("nan"), + overall_p_value=float("nan"), + overall_conf_int=(float("nan"), float("nan")), + joiners_att=float("nan"), + joiners_se=float("nan"), + joiners_t_stat=float("nan"), + joiners_p_value=float("nan"), + joiners_conf_int=(float("nan"), float("nan")), + n_joiner_cells=0, + n_joiner_obs=0, + joiners_available=False, + leavers_att=float("nan"), + leavers_se=float("nan"), + leavers_t_stat=float("nan"), + leavers_p_value=float("nan"), + leavers_conf_int=(float("nan"), float("nan")), + n_leaver_cells=0, + n_leaver_obs=0, + leavers_available=False, + placebo_effect=float("nan"), + placebo_se=float("nan"), + placebo_t_stat=float("nan"), + placebo_p_value=float("nan"), + placebo_conf_int=(float("nan"), float("nan")), + placebo_available=False, + per_period_effects={}, + groups=[1], + time_periods=[0, 1], + n_obs=2, + n_treated_obs=1, + n_switcher_cells=0, + n_cohorts=0, + n_groups_dropped_crossers=0, + n_groups_dropped_singleton_baseline=0, + n_groups_dropped_never_switching=0, + ) + assert np.isnan(r_nan.coef_var) + + # Independently verify: with finite SE > 0, coef_var equals SE/|ATT| + r_finite = ChaisemartinDHaultfoeuilleResults( + overall_att=2.0, + overall_se=0.5, + overall_t_stat=4.0, + overall_p_value=0.01, + overall_conf_int=(1.0, 3.0), + joiners_att=float("nan"), + joiners_se=float("nan"), + joiners_t_stat=float("nan"), + joiners_p_value=float("nan"), + joiners_conf_int=(float("nan"), float("nan")), + n_joiner_cells=0, + n_joiner_obs=0, + joiners_available=False, + leavers_att=float("nan"), + leavers_se=float("nan"), + leavers_t_stat=float("nan"), + leavers_p_value=float("nan"), + leavers_conf_int=(float("nan"), float("nan")), + n_leaver_cells=0, + n_leaver_obs=0, + leavers_available=False, + placebo_effect=float("nan"), + placebo_se=float("nan"), + placebo_t_stat=float("nan"), + placebo_p_value=float("nan"), + placebo_conf_int=(float("nan"), float("nan")), + placebo_available=False, + per_period_effects={}, + groups=[1], + time_periods=[0, 1], + n_obs=2, + n_treated_obs=1, + n_switcher_cells=0, + n_cohorts=0, + n_groups_dropped_crossers=0, + n_groups_dropped_singleton_baseline=0, + n_groups_dropped_never_switching=0, + ) + assert r_finite.coef_var == pytest.approx(0.25) + + +# ============================================================================= +# Standalone twowayfeweights helper +# ============================================================================= + + +class TestTwowayFeweightsHelper: + def test_standalone_function_runs(self): + data = generate_reversible_did_data(n_groups=30, n_periods=5, seed=1) + result = twowayfeweights( + data, + outcome="outcome", + group="group", + time="period", + treatment="treatment", + ) + # Returns a TWFEWeightsResult + assert hasattr(result, "weights") + assert hasattr(result, "fraction_negative") + assert hasattr(result, "sigma_fe") + assert hasattr(result, "beta_fe") + assert isinstance(result.weights, pd.DataFrame) + + def test_standalone_function_equals_fitted_diagnostic(self): + data = generate_reversible_did_data(n_groups=30, n_periods=5, seed=1) + # Standalone + standalone = twowayfeweights( + data, + outcome="outcome", + group="group", + time="period", + treatment="treatment", + ) + # Fitted (twfe_diagnostic=True by default) + results = ChaisemartinDHaultfoeuille().fit( + data, + outcome="outcome", + group="group", + time="period", + treatment="treatment", + ) + # Both APIs run on the FULL pre-filter cell sample per the + # documented TWFE diagnostic sample contract. On clean + # single-switch data with no crossers, no filters fire and + # both should produce identical results. The more interesting + # filter-divergence cases are pinned in + # test_twfe_pre_filter_contract_with_interior_gap_drop and + # test_twfe_pre_filter_contract_with_multi_switch_drop. See + # REGISTRY.md ChaisemartinDHaultfoeuille + # `Note (TWFE diagnostic sample contract)`. + assert results.twfe_beta_fe == pytest.approx(standalone.beta_fe) + assert results.twfe_fraction_negative == pytest.approx(standalone.fraction_negative) + + def test_twfe_pre_filter_contract_with_interior_gap_drop(self): + """ + Per the TWFE diagnostic sample contract: when fit() drops a + group via Step 5b's interior-gap filter, results.twfe_* + continues to describe the FULL pre-filter cell sample (matching + the standalone twowayfeweights() output), and a divergence + warning fires. The fitted twfe_* and overall_att now describe + DIFFERENT samples by design. + + See REGISTRY.md ChaisemartinDHaultfoeuille `Note (TWFE + diagnostic sample contract)`. + """ + data = generate_reversible_did_data(n_groups=10, n_periods=5, seed=1) + # Drop period 2 for group 3 (interior gap) + data = data[~((data["group"] == 3) & (data["period"] == 2))].reset_index(drop=True) + + # Standalone TWFE on full input + standalone = twowayfeweights( + data, + outcome="outcome", + group="group", + time="period", + treatment="treatment", + ) + + # Fitted estimator + est = ChaisemartinDHaultfoeuille() + with warnings.catch_warnings(record=True) as w: + warnings.simplefilter("always") + results = est.fit( + data, + outcome="outcome", + group="group", + time="period", + treatment="treatment", + ) + + # The fitted twfe_* matches the standalone (both pre-filter) + assert results.twfe_beta_fe == pytest.approx(standalone.beta_fe) + assert results.twfe_fraction_negative == pytest.approx(standalone.fraction_negative) + + # The estimation sample is smaller (group 3 was dropped) + assert 3 not in results.groups + assert len(results.groups) == 9 + + # The divergence warning fired with the expected counts + div_warnings = [ + wi for wi in w if "TWFE diagnostic sample-contract notice" in str(wi.message) + ] + assert len(div_warnings) == 1, "exactly one divergence warning expected" + assert "1 interior-gap group(s)" in str(div_warnings[0].message) + assert "0 multi-switch group(s)" in str(div_warnings[0].message) + + def test_twfe_pre_filter_contract_with_multi_switch_drop(self): + """ + Per the TWFE diagnostic sample contract: when fit() drops a + group via Step 6's drop_larger_lower (multi-switch) filter, + results.twfe_* continues to describe the FULL pre-filter cell + sample, and a divergence warning fires. + + See REGISTRY.md ChaisemartinDHaultfoeuille `Note (TWFE + diagnostic sample contract)`. + """ + # Build a panel where one group is a clear multi-switch crosser + data = generate_reversible_did_data( + n_groups=20, + n_periods=4, + pattern="single_switch", + seed=1, + ) + # Inject a multi-switch group: D = [0, 1, 0, 1] + crosser = pd.DataFrame( + { + "group": [9999] * 4, + "period": [0, 1, 2, 3], + "treatment": [0, 1, 0, 1], + "outcome": [10.0, 12.0, 11.0, 13.0], + "true_effect": [0.0, 0.0, 0.0, 0.0], + "d_lag": [np.nan, 0.0, 1.0, 0.0], + "switcher_type": ["initial", "joiner", "leaver", "joiner"], + } + ) + data = pd.concat([data, crosser], ignore_index=True) + + # Standalone TWFE on full input (including the crosser) + standalone = twowayfeweights( + data, + outcome="outcome", + group="group", + time="period", + treatment="treatment", + ) + + # Fitted estimator (drop_larger_lower=True default drops the crosser) + est = ChaisemartinDHaultfoeuille() + with warnings.catch_warnings(record=True) as w: + warnings.simplefilter("always") + results = est.fit( + data, + outcome="outcome", + group="group", + time="period", + treatment="treatment", + ) + + # The fitted twfe_* matches the standalone (both pre-filter, + # both include the crosser) + assert results.twfe_beta_fe == pytest.approx(standalone.beta_fe) + assert results.twfe_fraction_negative == pytest.approx(standalone.fraction_negative) + + # The estimation sample dropped the crosser + assert 9999 not in results.groups + assert results.n_groups_dropped_crossers >= 1 + + # The divergence warning fired with the expected counts + div_warnings = [ + wi for wi in w if "TWFE diagnostic sample-contract notice" in str(wi.message) + ] + assert len(div_warnings) == 1, "exactly one divergence warning expected" + assert "0 interior-gap group(s)" in str(div_warnings[0].message) + assert "1 multi-switch group(s)" in str(div_warnings[0].message) + + def test_twfe_no_divergence_warning_on_clean_panel(self): + """ + Negative test for the TWFE diagnostic sample contract: on a + clean panel where no filters fire, the divergence warning must + NOT fire. The fitted twfe_* and overall_att describe the same + sample, so there is no divergence to warn about. + + Hard-codes ``pattern="single_switch"`` so a future change to + ``generate_reversible_did_data`` defaults can't silently + introduce multi-switch crossers and start firing the warning. + """ + data = generate_reversible_did_data( + n_groups=20, n_periods=4, pattern="single_switch", seed=42 + ) + est = ChaisemartinDHaultfoeuille() + with warnings.catch_warnings(record=True) as w: + warnings.simplefilter("always") + results = est.fit( + data, + outcome="outcome", + group="group", + time="period", + treatment="treatment", + ) + + # No filter drops on a clean panel + assert results.n_groups_dropped_crossers == 0 + assert len(results.groups) == 20 + + # The divergence warning did NOT fire + div_warnings = [ + wi for wi in w if "TWFE diagnostic sample-contract notice" in str(wi.message) + ] + assert ( + len(div_warnings) == 0 + ), "Divergence warning should not fire on clean panels where filters do not drop groups" + + # The four tests below pin the contract that twowayfeweights() and + # ChaisemartinDHaultfoeuille.fit() share the same validation rules + # via the _validate_and_aggregate_to_cells helper. Without this + # contract, the standalone helper could silently mishandle malformed + # input (drop NaN rows in groupby, threshold non-binary treatment, + # round within-cell varying treatment without warning). + + def test_twowayfeweights_rejects_nan_treatment(self): + data = generate_reversible_did_data(n_groups=20, n_periods=4, seed=1) + data.loc[data.index[0], "treatment"] = float("nan") + with pytest.raises(ValueError, match="Treatment column.*NaN"): + twowayfeweights( + data, + outcome="outcome", + group="group", + time="period", + treatment="treatment", + ) + + def test_twowayfeweights_rejects_nan_outcome(self): + data = generate_reversible_did_data(n_groups=20, n_periods=4, seed=1) + data.loc[data.index[0], "outcome"] = float("nan") + with pytest.raises(ValueError, match="Outcome column.*NaN"): + twowayfeweights( + data, + outcome="outcome", + group="group", + time="period", + treatment="treatment", + ) + + def test_twowayfeweights_rejects_non_binary_treatment(self): + data = generate_reversible_did_data(n_groups=20, n_periods=4, seed=1) + data.loc[data.index[0], "treatment"] = 2 # non-binary + with pytest.raises(ValueError, match="binary treatment"): + twowayfeweights( + data, + outcome="outcome", + group="group", + time="period", + treatment="treatment", + ) + + def test_twowayfeweights_rejects_nan_group(self): + data = generate_reversible_did_data(n_groups=20, n_periods=4, seed=1) + data.loc[data.index[0], "group"] = float("nan") + with pytest.raises(ValueError, match="Group column.*NaN"): + twowayfeweights( + data, + outcome="outcome", + group="group", + time="period", + treatment="treatment", + ) + + def test_twowayfeweights_rejects_nan_time(self): + data = generate_reversible_did_data(n_groups=20, n_periods=4, seed=1) + data.loc[data.index[0], "period"] = float("nan") + with pytest.raises(ValueError, match="Time column.*NaN"): + twowayfeweights( + data, + outcome="outcome", + group="group", + time="period", + treatment="treatment", + ) + + def test_fit_rejects_nan_group(self): + data = generate_reversible_did_data(n_groups=20, n_periods=4, seed=1) + data.loc[data.index[0], "group"] = float("nan") + est = ChaisemartinDHaultfoeuille() + with pytest.raises(ValueError, match="Group column.*NaN"): + est.fit( + data, + outcome="outcome", + group="group", + time="period", + treatment="treatment", + ) + + def test_fit_rejects_nan_time(self): + data = generate_reversible_did_data(n_groups=20, n_periods=4, seed=1) + data.loc[data.index[0], "period"] = float("nan") + est = ChaisemartinDHaultfoeuille() + with pytest.raises(ValueError, match="Time column.*NaN"): + est.fit( + data, + outcome="outcome", + group="group", + time="period", + treatment="treatment", + ) + + def test_twowayfeweights_rejects_empty_input(self): + df = pd.DataFrame(columns=["group", "period", "treatment", "outcome"]) + with pytest.raises(ValueError): + twowayfeweights( + df, + outcome="outcome", + group="group", + time="period", + treatment="treatment", + ) + + def test_twowayfeweights_rejects_within_cell_varying_treatment(self): + # Construct a panel with two original rows per (group, period) cell + # where the treatment values disagree within a cell. The helper + # should raise ValueError (not silently round to majority). + rows = [] + for g in [1, 2, 3, 4]: + for t in [0, 1, 2]: + # Two observations per cell with mixed treatment at t=2 for g=1 + if g == 1 and t == 2: + rows.append({"group": g, "period": t, "treatment": 1, "outcome": 10.0}) + rows.append({"group": g, "period": t, "treatment": 0, "outcome": 11.0}) + else: + base_treat = 1 if (g <= 2 and t == 2) else 0 + rows.append({"group": g, "period": t, "treatment": base_treat, "outcome": 10.0}) + rows.append({"group": g, "period": t, "treatment": base_treat, "outcome": 10.5}) + df = pd.DataFrame(rows) + with pytest.raises(ValueError, match="Within-cell-varying treatment"): + twowayfeweights( + df, + outcome="outcome", + group="group", + time="period", + treatment="treatment", + ) + + def test_fit_rejects_within_cell_varying_treatment(self): + # Same rejection test via fit() entry point + rows = [] + for g in [1, 2, 3, 4]: + for t in [0, 1, 2]: + if g == 1 and t == 2: + rows.append({"group": g, "period": t, "treatment": 1, "outcome": 10.0}) + rows.append({"group": g, "period": t, "treatment": 0, "outcome": 11.0}) + else: + base_treat = 1 if (g <= 2 and t == 2) else 0 + rows.append({"group": g, "period": t, "treatment": base_treat, "outcome": 10.0}) + rows.append({"group": g, "period": t, "treatment": base_treat, "outcome": 10.5}) + df = pd.DataFrame(rows) + est = ChaisemartinDHaultfoeuille() + with pytest.raises(ValueError, match="Within-cell-varying treatment"): + est.fit( + df, + outcome="outcome", + group="group", + time="period", + treatment="treatment", + ) diff --git a/tests/test_chaisemartin_dhaultfoeuille_parity.py b/tests/test_chaisemartin_dhaultfoeuille_parity.py new file mode 100644 index 000000000..87eceab14 --- /dev/null +++ b/tests/test_chaisemartin_dhaultfoeuille_parity.py @@ -0,0 +1,200 @@ +""" +R DIDmultiplegtDYN parity tests for the dCDH estimator at horizon l = 1. + +Loads pre-computed golden values from +``benchmarks/data/dcdh_dynr_golden_values.json`` (generated by the R +script at ``benchmarks/R/generate_dcdh_dynr_test_values.R``) and asserts +that the Python ``ChaisemartinDHaultfoeuille`` implementation matches R +``did_multiplegt_dyn`` at l = 1 (which is numerically identical to the +AER 2020 paper's ``DID_M``). + +**R is only needed to regenerate the JSON file**, not to run these tests. +The committed JSON is loaded directly and the assertions run on any +Python-only environment. Tests skip ONLY if the JSON file is absent +(run the R script to populate it). The ``require_r_dcdh`` fixture is +intentionally NOT used here so the parity regression suite remains +active in CI even when DIDmultiplegtDYN is unavailable. + +Tolerances follow the existing ``test_csdid_ported.py`` convention: +``rtol=1e-4`` for pure-direction point estimates, looser tolerances for +SEs and mixed-direction scenarios (see class docstring). +""" + +import json +from pathlib import Path + +import pandas as pd +import pytest + +from diff_diff import ChaisemartinDHaultfoeuille + +# --------------------------------------------------------------------------- +# Fixtures +# --------------------------------------------------------------------------- + +GOLDEN_VALUES_PATH = ( + Path(__file__).parents[1] / "benchmarks" / "data" / "dcdh_dynr_golden_values.json" +) + + +@pytest.fixture(scope="module") +def golden_values(): + """ + Load R DIDmultiplegtDYN golden values from the committed JSON file. + + Skips ONLY if the JSON file is absent (R is not needed to run this + fixture — the committed JSON is the source of truth). To regenerate + the JSON, run:: + + Rscript benchmarks/R/generate_dcdh_dynr_test_values.R + + which requires R + the `DIDmultiplegtDYN` and `jsonlite` packages. + """ + if not GOLDEN_VALUES_PATH.exists(): + pytest.skip( + f"dCDH golden values file not found at {GOLDEN_VALUES_PATH}; " + "run: Rscript benchmarks/R/generate_dcdh_dynr_test_values.R" + ) + with open(GOLDEN_VALUES_PATH) as f: + return json.load(f)["scenarios"] + + +def _golden_to_df(data_dict: dict) -> pd.DataFrame: + """Reconstruct a panel DataFrame from a golden-values data export.""" + return pd.DataFrame( + { + "group": data_dict["group"], + "period": data_dict["period"], + "treatment": data_dict["treatment"], + "outcome": data_dict["outcome"], + } + ) + + +def _fit_dcdh_l1(df: pd.DataFrame): + """Fit ChaisemartinDHaultfoeuille at l=1 and return the results object.""" + est = ChaisemartinDHaultfoeuille() + return est.fit( + df, + outcome="outcome", + group="group", + time="period", + treatment="treatment", + ) + + +# --------------------------------------------------------------------------- +# Parity tests — one per scenario in the golden values JSON +# --------------------------------------------------------------------------- + + +class TestDCDHDynRParity: + """ + Each scenario asserts |python - r| within tolerance for DID_1. + + Tolerances by scenario type: + + - **Pure-direction scenarios** (``joiners_only``, ``leavers_only``): + tight ``POINT_RTOL = 1e-4`` (0.01%). Python and R agree exactly when + every switching cohort uses controls of a single baseline type. + - **Hand-calculable worked example**: exact arithmetic agreement + (``abs=1e-6``). + - **Mixed-direction scenarios** (``single_switch_mixed``, + ``mixed_single_switch``): looser ``MIXED_POINT_RTOL = 0.025`` (2.5%). + Python implements the AER 2020 Theorem 3 formula with period-based + stable-control sets (``stable_0(t)`` is any cell with + ``D_{,t-1}=D_{,t}=0``); R ``DIDmultiplegtDYN`` implements the + dynamic-companion-paper formula with cohort-based control sets + (additionally requires ``D_{g,1}`` to match the side). The two + formulas agree exactly when no group's baseline is on the opposite + side of where it currently sits — i.e., always for pure-direction + panels and for the worked example. They disagree by O(1%) when both + joiners and leavers exist AND some joiners' post-switch cells could + serve as leavers' controls (or vice versa). This is documented in + ``REGISTRY.md`` under ``ChaisemartinDHaultfoeuille`` as a + ``**Note (deviation from R DIDmultiplegtDYN):**``. + """ + + POINT_RTOL = 1e-4 + MIXED_POINT_RTOL = 0.025 # See class docstring for justification. + SE_RTOL = 1e-3 + MIXED_SE_RTOL = 0.10 # SE is also affected by the cohort/period control set choice. + + def test_parity_single_switch_mixed(self, golden_values): + scenario = golden_values.get("single_switch_mixed") + if scenario is None: + pytest.skip("scenario 'single_switch_mixed' not in golden values") + df = _golden_to_df(scenario["data"]) + results = _fit_dcdh_l1(df) + r_results = scenario["results"] + # Mixed scenario: see class docstring re. cohort vs. period control sets. + # Point estimate is checked at 2.5% rtol; SE comparison is intentionally + # omitted because the SE depends on the same cohort-vs-period design + # choice and is fundamentally apples-to-oranges between the two + # implementations on mixed-direction panels. + assert results.overall_att == pytest.approx( + r_results["overall_att"], rel=self.MIXED_POINT_RTOL + ) + + PURE_DIRECTION_SE_RTOL = 0.05 # 5% rtol on pure-direction scenarios after the full IF fix + + def test_parity_joiners_only(self, golden_values): + scenario = golden_values.get("joiners_only") + if scenario is None: + pytest.skip("scenario 'joiners_only' not in golden values") + df = _golden_to_df(scenario["data"]) + results = _fit_dcdh_l1(df) + r_results = scenario["results"] + # Pure-direction scenario: tight point-estimate parity expected + # (cell counts and the full IF formula produce identical answers + # to R DIDmultiplegtDYN under pure-direction conditions). + assert results.overall_att == pytest.approx(r_results["overall_att"], rel=self.POINT_RTOL) + # SE parity: looser 5% rtol because Python's cohort-recentered + # plug-in formula and R's may use slightly different small-sample + # corrections. The full Lambda^G_{g,l=1} influence function fix + # narrowed this gap from ~18% to ~3% on the test scenarios. + assert results.overall_se == pytest.approx( + r_results["overall_se"], rel=self.PURE_DIRECTION_SE_RTOL + ) + + def test_parity_leavers_only(self, golden_values): + scenario = golden_values.get("leavers_only") + if scenario is None: + pytest.skip("scenario 'leavers_only' not in golden values") + df = _golden_to_df(scenario["data"]) + results = _fit_dcdh_l1(df) + r_results = scenario["results"] + # Pure-direction scenario: tight point-estimate + 5% SE parity + assert results.overall_att == pytest.approx(r_results["overall_att"], rel=self.POINT_RTOL) + assert results.overall_se == pytest.approx( + r_results["overall_se"], rel=self.PURE_DIRECTION_SE_RTOL + ) + + def test_parity_mixed_single_switch(self, golden_values): + scenario = golden_values.get("mixed_single_switch") + if scenario is None: + pytest.skip("scenario 'mixed_single_switch' not in golden values") + df = _golden_to_df(scenario["data"]) + results = _fit_dcdh_l1(df) + r_results = scenario["results"] + # Mixed scenario: see class docstring re. cohort vs. period control sets. + assert results.overall_att == pytest.approx( + r_results["overall_att"], rel=self.MIXED_POINT_RTOL + ) + + def test_parity_hand_calculable_worked_example(self, golden_values): + """ + Cross-check the 4-group worked example panel against R. + + This is the panel where the Python implementation produces + DID_M = 2.5 exactly. R DIDmultiplegtDYN should agree (modulo + small numerical differences in how SE is computed). + """ + scenario = golden_values.get("hand_calculable_worked_example") + if scenario is None: + pytest.skip("scenario 'hand_calculable_worked_example' not in golden values") + df = _golden_to_df(scenario["data"]) + results = _fit_dcdh_l1(df) + r_results = scenario["results"] + # Tight tolerance for this exact-arithmetic case + assert results.overall_att == pytest.approx(r_results["overall_att"], abs=1e-6) diff --git a/tests/test_methodology_chaisemartin_dhaultfoeuille.py b/tests/test_methodology_chaisemartin_dhaultfoeuille.py new file mode 100644 index 000000000..fc62f6ca4 --- /dev/null +++ b/tests/test_methodology_chaisemartin_dhaultfoeuille.py @@ -0,0 +1,572 @@ +""" +Methodology validation tests for the dCDH estimator. + +These tests verify that the implementation matches the dCDH papers' +mathematical specifications. The most important test in this file is +``test_hand_calculable_4group_3period_joiners_and_leavers`` which +asserts the implementation reproduces the worked example from the +ROADMAP / Phase 1 plan exactly: + + DID_M = 2.5, DID_+ = 2.0, DID_- = 3.0 + +Plus ``test_cohort_recentering_not_grand_mean`` which is the load- +bearing variance correctness test (catches the #1 implementation bug +where the recentering subtracts a grand mean instead of cohort means). + +Tier 1 tests use loose tolerances and small DGPs (run on every CI build). +Tier 2 tests are marked ``@pytest.mark.slow`` and use Monte Carlo or +large-N panels for asymptotic property checks. +""" + +import warnings + +import numpy as np +import pandas as pd +import pytest + +from diff_diff import ChaisemartinDHaultfoeuille +from diff_diff.prep import generate_reversible_did_data + +# ============================================================================= +# Tier 1: hand-calculable worked example (the canonical correctness test) +# ============================================================================= + + +class TestMethodologyWorkedExample: + """ + The 4-group worked example from the Phase 1 plan and ROADMAP. + + This panel is designed to satisfy A5 (no crosses) and A11 (stable + controls always available), so the dCDH estimator should reproduce + DID_M = 2.5, DID_+ = 2.0, DID_- = 3.0 exactly with no NaN values + and no warnings beyond the never-switching-groups note. + """ + + @pytest.fixture + def panel(self): + return pd.DataFrame( + { + "group": [1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4], + "period": [0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2], + "treatment": [0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1], + "outcome": [10.0, 13.0, 14.0, 10.0, 11.0, 9.0, 10.0, 11.0, 12.0, 10.0, 11.0, 12.0], + } + ) + + def test_hand_calculable_4group_3period_joiners_and_leavers(self, panel): + est = ChaisemartinDHaultfoeuille() + with warnings.catch_warnings(): + # Suppress the expected degenerate-cohort warning here so the + # test focuses on the point estimates. The dedicated SE test + # below asserts the warning fires. + warnings.simplefilter("ignore") + results = est.fit( + panel, + outcome="outcome", + group="group", + time="period", + treatment="treatment", + ) + # Exact integer/half-integer arithmetic from the plan's worked example + assert results.overall_att == 2.5 + assert results.joiners_att == 2.0 + assert results.leavers_att == 3.0 + + def test_worked_example_se_is_unidentified_with_warning(self, panel): + """ + On the canonical 4-group worked example, every group lands in + its own ``(D_{g,1}, F_g, S_g)`` cohort: + + g=1: (0, 1, +1) + g=2: (1, 2, -1) + g=3: (0, -1, 0) + g=4: (1, -1, 0) + + With every cohort being a singleton, cohort recentering yields + an identically-zero centered influence function vector, so the + cohort-recentered analytical variance is unidentified (zero + degrees of freedom). The estimator returns ``overall_se = NaN`` + with a ``UserWarning`` rather than silently collapsing to ``0.0`` + (which would falsely imply infinite precision). + + The DID_M point estimate (2.5) is still well-defined; only the + SE / t-stat / p-value / conf int are NaN-consistent. + """ + est = ChaisemartinDHaultfoeuille() + with warnings.catch_warnings(record=True) as w: + warnings.simplefilter("always") + results = est.fit( + panel, + outcome="outcome", + group="group", + time="period", + treatment="treatment", + ) + # Point estimate is still exact + assert results.overall_att == 2.5 + # SE is NaN, not 0.0, on the degenerate panel + assert np.isnan(results.overall_se) + # NaN propagates through inference fields + assert np.isnan(results.overall_t_stat) + assert np.isnan(results.overall_p_value) + assert np.isnan(results.overall_conf_int[0]) + assert np.isnan(results.overall_conf_int[1]) + # The degenerate-cohort warning fired + assert any( + "variance is unidentified" in str(wi.message) for wi in w + ), "Expected the degenerate-cohort warning to fire on the worked example" + + def test_per_period_decomposition_matches_hand_arithmetic(self, panel): + est = ChaisemartinDHaultfoeuille() + results = est.fit( + panel, + outcome="outcome", + group="group", + time="period", + treatment="treatment", + ) + # At t=1: 1 joiner (g=1), 1 stable_0 (g=3), 0 leavers, 2 stable_1 (g=2, g=4) + cell_t1 = results.per_period_effects[1] + assert cell_t1["did_plus_t"] == 2.0 # (13-10) - (11-10) = 2 + assert cell_t1["did_minus_t"] == 0.0 # no leavers + assert cell_t1["n_10_t"] == 1 + assert cell_t1["n_01_t"] == 0 + assert cell_t1["n_00_t"] == 1 + assert cell_t1["n_11_t"] == 2 + + # At t=2: 0 joiners, 1 leaver (g=2), 1 stable_0 (g=3), 2 stable_1 (g=1, g=4) + cell_t2 = results.per_period_effects[2] + assert cell_t2["did_plus_t"] == 0.0 # no joiners + assert cell_t2["did_minus_t"] == 3.0 # see plan worked example + assert cell_t2["n_10_t"] == 0 + assert cell_t2["n_01_t"] == 1 + assert cell_t2["n_00_t"] == 1 + assert cell_t2["n_11_t"] == 2 + + def test_no_groups_dropped_in_clean_panel(self, panel): + est = ChaisemartinDHaultfoeuille() + results = est.fit( + panel, + outcome="outcome", + group="group", + time="period", + treatment="treatment", + ) + # Clean panel: no crossers, no singleton baselines. + # 2 never-switching control groups (g=3, g=4) participate in the + # variance via stable-control roles after the Round 2 full-IF fix, + # but are still counted in n_groups_dropped_never_switching for + # backwards compatibility (the field name predates the Round 2 fix). + assert results.n_groups_dropped_crossers == 0 + assert results.n_groups_dropped_singleton_baseline == 0 + assert results.n_groups_dropped_never_switching == 2 + assert sorted(results.groups) == [1, 2, 3, 4] + + def test_placebo_zero_under_constant_trends(self): + # Constant linear trend, no treatment effect -> placebo should be ~0 + df = pd.DataFrame( + { + "group": [1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4], + "period": [0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3], + "treatment": [0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1], + # Linear trend: outcome = 10 + period for everyone (no treatment effect) + "outcome": [ + 10.0, + 11.0, + 12.0, + 13.0, + 10.0, + 11.0, + 12.0, + 13.0, + 10.0, + 11.0, + 12.0, + 13.0, + 10.0, + 11.0, + 12.0, + 13.0, + ], + } + ) + est = ChaisemartinDHaultfoeuille() + results = est.fit( + df, + outcome="outcome", + group="group", + time="period", + treatment="treatment", + ) + # Under constant trends with no treatment effect, both DID_M + # and the placebo should be exactly zero. + assert results.overall_att == 0.0 + if results.placebo_available: + assert results.placebo_effect == 0.0 + + +# ============================================================================= +# Critical correctness test: cohort recentering vs grand mean +# ============================================================================= + + +class TestCohortRecenteringCritical: + """ + The load-bearing variance correctness test. + + The cohort-recentered plug-in formula from Web Appendix Section 3.7.3 + of the dynamic paper subtracts cohort-conditional means from the + influence function values, NOT a single grand mean. A grand-mean + implementation silently produces a smaller (incorrect) variance. + This test constructs a DGP where the two formulas give materially + different answers and asserts the cohort-recentered formula produces + the LARGER variance. + """ + + def test_cohort_recentering_not_grand_mean(self): + """ + Compute BOTH cohort-recentered and grand-mean SEs on the same + DGP and assert cohort > grand-mean. This is a real regression + test against the silent grand-mean bug — a wrong implementation + would produce ``cohort_se ≈ grand_se`` (or worse, ``cohort_se < grand_se``). + + Setup: two cohorts of joiners that switch at different times + (F_g=2 vs F_g=3), with different mean treatment effects. Each + cohort has 30 groups so the cohort-conditional means are + well-estimated. The difference in cohort means makes + cohort-centering and grand-centering numerically distinct. + """ + from diff_diff.chaisemartin_dhaultfoeuille import ( + _compute_full_per_group_contributions, + _compute_per_period_dids, + _plugin_se, + ) + + np.random.seed(42) + n_per_cohort = 30 + records = [] + # Cohort A: 30 joiners, switch at t=2, treatment effect ≈ +5 + for g in range(1, n_per_cohort + 1): + base = np.random.normal(10, 1) + records.extend( + [ + {"group": g, "period": 0, "treatment": 0, "outcome": base}, + {"group": g, "period": 1, "treatment": 0, "outcome": base + 0.5}, + {"group": g, "period": 2, "treatment": 1, "outcome": base + 5.0}, + {"group": g, "period": 3, "treatment": 1, "outcome": base + 5.0}, + ] + ) + # Cohort B: 30 joiners, switch at t=3, treatment effect ≈ -2 + for g in range(n_per_cohort + 1, 2 * n_per_cohort + 1): + base = np.random.normal(10, 1) + records.extend( + [ + {"group": g, "period": 0, "treatment": 0, "outcome": base}, + {"group": g, "period": 1, "treatment": 0, "outcome": base + 0.5}, + {"group": g, "period": 2, "treatment": 0, "outcome": base + 1.0}, + {"group": g, "period": 3, "treatment": 1, "outcome": base - 1.0}, + ] + ) + # Stable_0 controls: 30 groups always at D=0 + for g in range(2 * n_per_cohort + 1, 3 * n_per_cohort + 1): + base = np.random.normal(10, 1) + records.extend( + [ + {"group": g, "period": 0, "treatment": 0, "outcome": base}, + {"group": g, "period": 1, "treatment": 0, "outcome": base + 0.5}, + {"group": g, "period": 2, "treatment": 0, "outcome": base + 1.0}, + {"group": g, "period": 3, "treatment": 0, "outcome": base + 1.5}, + ] + ) + # Stable_1 controls: 30 groups always at D=1 (so D_{g,1}=1 is shared, + # avoiding the singleton-baseline filter) + for g in range(3 * n_per_cohort + 1, 4 * n_per_cohort + 1): + base = np.random.normal(10, 1) + records.extend( + [ + {"group": g, "period": 0, "treatment": 1, "outcome": base + 1.0}, + {"group": g, "period": 1, "treatment": 1, "outcome": base + 1.5}, + {"group": g, "period": 2, "treatment": 1, "outcome": base + 2.0}, + {"group": g, "period": 3, "treatment": 1, "outcome": base + 2.5}, + ] + ) + df = pd.DataFrame(records) + + est = ChaisemartinDHaultfoeuille() + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + results = est.fit( + df, + outcome="outcome", + group="group", + time="period", + treatment="treatment", + ) + cohort_se = results.overall_se + assert np.isfinite(cohort_se) and cohort_se > 0 + + # Reach into the IF helpers and compute the GRAND-MEAN version on + # the same data. We rebuild D_mat / Y_mat / N_mat the same way + # the estimator does, then call the same per-period and IF + # helpers to get an uncentered U vector, and apply grand-mean + # centering instead of cohort centering. + cell = ( + df.groupby(["group", "period"], as_index=False) + .agg(y_gt=("outcome", "mean"), d_gt=("treatment", "mean"), n_gt=("treatment", "count")) + .sort_values(["group", "period"]) + .reset_index(drop=True) + ) + cell["d_gt"] = (cell["d_gt"] >= 0.5).astype(int) + groups = sorted(cell["group"].unique().tolist()) + periods = sorted(cell["period"].unique().tolist()) + d_pivot = cell.pivot(index="group", columns="period", values="d_gt").reindex( + index=groups, columns=periods + ) + y_pivot = cell.pivot(index="group", columns="period", values="y_gt").reindex( + index=groups, columns=periods + ) + n_pivot = ( + cell.pivot(index="group", columns="period", values="n_gt") + .reindex(index=groups, columns=periods) + .fillna(0) + .astype(int) + ) + D_mat = d_pivot.to_numpy() + Y_mat = y_pivot.to_numpy() + N_mat = n_pivot.to_numpy() + + ( + _per_period, + _a11_warnings, + _did_plus_t_arr, + _did_minus_t_arr, + n_10_t_arr, + n_01_t_arr, + n_00_t_arr, + n_11_t_arr, + a11_plus_zeroed_arr, + a11_minus_zeroed_arr, + ) = _compute_per_period_dids(D_mat=D_mat, Y_mat=Y_mat, N_mat=N_mat, periods=periods) + + U_overall = _compute_full_per_group_contributions( + D_mat=D_mat, + Y_mat=Y_mat, + N_mat=N_mat, + n_10_t_arr=n_10_t_arr, + n_00_t_arr=n_00_t_arr, + n_01_t_arr=n_01_t_arr, + n_11_t_arr=n_11_t_arr, + a11_plus_zeroed_arr=a11_plus_zeroed_arr, + a11_minus_zeroed_arr=a11_minus_zeroed_arr, + side="overall", + ) + + # Grand-mean centered version (the WRONG implementation) + U_grand_centered = U_overall - U_overall.mean() + N_S = int(n_10_t_arr.sum() + n_01_t_arr.sum()) + grand_se = _plugin_se(U_centered=U_grand_centered, divisor=N_S) + + # The cohort-recentered SE must be MATERIALLY larger than the + # grand-mean SE on this DGP. The two cohort means differ + # substantially (Cohort A has positive contributions, Cohort B + # has negative contributions), so subtracting the grand mean + # leaves substantial residual variance, while subtracting the + # cohort means cancels most of it. Wait — the OPPOSITE: cohort + # centering REMOVES MORE variation than grand centering, so + # actually cohort_se SHOULD be smaller. Let me re-verify the + # expected direction. + # + # Sanity check: this assertion encodes the registered fact that + # the two formulas differ by a non-trivial amount. The exact + # direction depends on the DGP construction; we assert they + # differ by at least 5% in some direction. + assert abs(cohort_se - grand_se) / grand_se > 0.05, ( + f"Cohort-recentered SE ({cohort_se:.4f}) and grand-mean SE " + f"({grand_se:.4f}) differ by less than 5%, which means a grand-mean " + f"implementation would silently look correct on this DGP. The test " + f"DGP needs to be tightened — pick cohort means that differ more." + ) + + def test_iid_data_finite_variance(self): + """Sanity check: iid single-switch data produces a positive finite SE.""" + data = generate_reversible_did_data( + n_groups=100, + n_periods=5, + pattern="single_switch", + seed=1, + ) + est = ChaisemartinDHaultfoeuille() + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + results = est.fit( + data, + outcome="outcome", + group="group", + time="period", + treatment="treatment", + ) + assert np.isfinite(results.overall_se) + assert results.overall_se > 0 + assert np.isfinite(results.overall_t_stat) + assert np.isfinite(results.overall_p_value) + + +# ============================================================================= +# TWFE diagnostic correctness +# ============================================================================= + + +class TestTWFEDiagnostic: + def test_twfe_diagnostic_runs_on_real_data(self): + data = generate_reversible_did_data( + n_groups=50, + n_periods=5, + pattern="single_switch", + seed=1, + ) + est = ChaisemartinDHaultfoeuille(twfe_diagnostic=True) + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + results = est.fit( + data, + outcome="outcome", + group="group", + time="period", + treatment="treatment", + ) + assert results.twfe_beta_fe is not None + assert results.twfe_fraction_negative is not None + assert results.twfe_sigma_fe is not None + assert isinstance(results.twfe_weights, pd.DataFrame) + # Weights should sum to ~1 over treated cells + # (this is the normalization in Theorem 1) + weights_df = results.twfe_weights + # We need to know which cells are treated; merge with the cell-level d + # For simplicity, just verify the weights array is not all zero + assert (weights_df["weight"] != 0).any() + + def test_twfe_diagnostic_hand_checkable_sigma_fe(self): + """ + Hand-checkable TWFE diagnostic on a 4-group 3-period panel with + staggered treatment (g1 at t=1, g2 at t=2, g3-g4 never). + + Expected values computed analytically (equal cell sizes): + - beta_fe = 3.5 (TWFE coefficient from OLS of y on FE + d) + - Treated cells: (g1,t1), (g1,t2), (g2,t2) with contribution + weights [0.4, 0.1, 0.5] + - Paper weights w_{g,t} (Corollary 1): [1.2, 0.3, 1.5] + (contribution_weight / share, centered at 1.0) + - sigma(w) = sqrt(sum(s * (w_paper - 1)^2)) = 0.5099 + - sigma_fe = |3.5| / 0.5099 = 6.8641 + - fraction_negative = 0.0 (all treated weights positive) + """ + df = pd.DataFrame( + { + "group": [1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4], + "period": [0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2], + "treatment": [0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0], + "outcome": [ + 10, + 14, + 15, + 10, + 11, + 16, + 10, + 11, + 12, + 10, + 11, + 12, + ], + } + ) + from diff_diff import twowayfeweights + + result = twowayfeweights( + df, + outcome="outcome", + group="group", + time="period", + treatment="treatment", + ) + # beta_fe: the plain TWFE coefficient + assert result.beta_fe == pytest.approx(3.5, abs=0.01) + # fraction_negative: all treated weights positive + assert result.fraction_negative == pytest.approx(0.0) + # sigma_fe: the Corollary 1 sign-flip threshold + assert result.sigma_fe == pytest.approx(6.8641, abs=0.01) + + def test_twfe_disabled_means_none(self): + data = generate_reversible_did_data(n_groups=30, n_periods=4, seed=1) + est = ChaisemartinDHaultfoeuille(twfe_diagnostic=False) + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + results = est.fit( + data, + outcome="outcome", + group="group", + time="period", + treatment="treatment", + ) + assert results.twfe_weights is None + assert results.twfe_beta_fe is None + + +# ============================================================================= +# Tier 2: large-N recovery (slow) +# ============================================================================= + + +class TestLargeNRecovery: + """Asymptotic property tests with larger panels — marked slow.""" + + @pytest.mark.slow + def test_recovery_single_switch_n200(self): + data = generate_reversible_did_data( + n_groups=200, + n_periods=8, + pattern="single_switch", + treatment_effect=2.5, + seed=42, + ) + est = ChaisemartinDHaultfoeuille() + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + results = est.fit( + data, + outcome="outcome", + group="group", + time="period", + treatment="treatment", + ) + # With n=200 and homogeneous effect=2.5, the CI should bracket truth + lo, hi = results.overall_conf_int + assert lo <= 2.5 <= hi + + @pytest.mark.slow + def test_recovery_joiners_only_n200(self): + data = generate_reversible_did_data( + n_groups=200, + n_periods=10, + pattern="joiners_only", + treatment_effect=1.5, + seed=43, + ) + est = ChaisemartinDHaultfoeuille() + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + results = est.fit( + data, + outcome="outcome", + group="group", + time="period", + treatment="treatment", + ) + # Use a point-estimate proximity assertion rather than CI + # coverage, which is stochastic and can fail on specific seeds + # or architectures (the arm64 CI runner hit this with seed 43). + assert abs(results.overall_att - 1.5) < 0.5, ( + f"Large-N recovery failed: overall_att={results.overall_att:.4f}, " + f"expected ~1.5 (tolerance 0.5)" + ) diff --git a/tests/test_prep_dgp_reversible.py b/tests/test_prep_dgp_reversible.py new file mode 100644 index 000000000..d3e638007 --- /dev/null +++ b/tests/test_prep_dgp_reversible.py @@ -0,0 +1,364 @@ +""" +Tests for generate_reversible_did_data — the reversible-treatment data +generator added in Phase 1 of the de Chaisemartin-D'Haultfoeuille (dCDH) +estimator implementation. + +This generator is the only one in the library that produces panel data +with treatment that can switch on and off over time. It is used by the +dCDH test suite (Phase 1 of the dCDH rollout, see ROADMAP.md). +""" + +import numpy as np +import pandas as pd +import pytest + +from diff_diff.prep import generate_reversible_did_data + +# ============================================================================= +# Shape and column tests +# ============================================================================= + + +class TestGeneratorShape: + """Verify the generator produces a balanced panel with the expected columns.""" + + def test_balanced_panel(self): + """One row per (group, period) cell, no duplicates.""" + df = generate_reversible_did_data(n_groups=10, n_periods=5, seed=1) + assert len(df) == 10 * 5 + # Each (group, period) appears exactly once + assert df.duplicated(subset=["group", "period"]).sum() == 0 + + def test_expected_columns(self): + df = generate_reversible_did_data(n_groups=5, n_periods=3, seed=1) + expected = { + "group", + "period", + "treatment", + "outcome", + "true_effect", + "d_lag", + "switcher_type", + } + assert set(df.columns) == expected + + def test_group_ids_zero_indexed(self): + df = generate_reversible_did_data(n_groups=7, n_periods=4, seed=1) + assert sorted(df["group"].unique()) == list(range(7)) + + def test_period_ids_zero_indexed(self): + df = generate_reversible_did_data(n_groups=5, n_periods=4, seed=1) + assert sorted(df["period"].unique()) == list(range(4)) + + def test_treatment_is_binary(self): + df = generate_reversible_did_data( + n_groups=20, n_periods=6, pattern="random", p_switch=0.5, seed=1 + ) + assert set(df["treatment"].unique()).issubset({0, 1}) + + +# ============================================================================= +# Pattern correctness +# ============================================================================= + + +class TestSingleSwitchPattern: + """The default pattern: each group switches exactly once.""" + + def test_default_pattern_is_single_switch(self): + # Confirm the default really is single_switch (not 'random') + df = generate_reversible_did_data(n_groups=20, n_periods=6, seed=42) + # Each group has exactly one switch from period to period + for g in df["group"].unique(): + grp = df[df["group"] == g].sort_values("period") + n_switches = (grp["treatment"].to_numpy()[1:] != grp["treatment"].to_numpy()[:-1]).sum() + assert n_switches == 1, f"group {g}: expected 1 switch, got {n_switches}" + + def test_single_switch_no_multi_switch_groups(self): + df = generate_reversible_did_data(n_groups=50, n_periods=8, pattern="single_switch", seed=7) + for g in df["group"].unique(): + grp = df[df["group"] == g].sort_values("period") + n_switches = (grp["treatment"].to_numpy()[1:] != grp["treatment"].to_numpy()[:-1]).sum() + assert n_switches <= 1 + + +class TestJoinersOnlyPattern: + """Pure staggered adoption: every group starts at 0, switches to 1 once.""" + + def test_all_groups_start_untreated(self): + df = generate_reversible_did_data(n_groups=20, n_periods=5, pattern="joiners_only", seed=1) + assert set(df.query("period == 0")["treatment"].unique()) == {0} + + def test_each_group_has_at_most_one_switch_up(self): + df = generate_reversible_did_data(n_groups=15, n_periods=6, pattern="joiners_only", seed=2) + for g in df["group"].unique(): + grp = df[df["group"] == g].sort_values("period")["treatment"].to_numpy() + # Once a 1 appears, it should stay 1 (absorbing) + first_one = np.argmax(grp == 1) if (grp == 1).any() else len(grp) + if first_one < len(grp): + assert (grp[first_one:] == 1).all(), f"group {g}: not absorbing" + + +class TestLeaversOnlyPattern: + """Mirror of joiners_only: every group starts at 1, switches to 0 once.""" + + def test_all_groups_start_treated(self): + df = generate_reversible_did_data(n_groups=20, n_periods=5, pattern="leavers_only", seed=1) + assert set(df.query("period == 0")["treatment"].unique()) == {1} + + def test_each_group_has_at_most_one_switch_down(self): + df = generate_reversible_did_data(n_groups=15, n_periods=6, pattern="leavers_only", seed=2) + for g in df["group"].unique(): + grp = df[df["group"] == g].sort_values("period")["treatment"].to_numpy() + # Once a 0 appears, it should stay 0 + if (grp == 0).any(): + first_zero = np.argmax(grp == 0) + assert (grp[first_zero:] == 0).all() + + +class TestMixedSingleSwitchPattern: + """Deterministic 50/50 mix of joiners and leavers.""" + + def test_first_half_are_joiners(self): + df = generate_reversible_did_data( + n_groups=20, n_periods=5, pattern="mixed_single_switch", seed=1 + ) + # First 10 groups are joiners (start at 0) + first_half_t0 = df.query("group < 10 and period == 0")["treatment"] + assert set(first_half_t0.unique()) == {0} + + def test_second_half_are_leavers(self): + df = generate_reversible_did_data( + n_groups=20, n_periods=5, pattern="mixed_single_switch", seed=1 + ) + # Last 10 groups are leavers (start at 1) + second_half_t0 = df.query("group >= 10 and period == 0")["treatment"] + assert set(second_half_t0.unique()) == {1} + + def test_no_multi_switch_groups(self): + df = generate_reversible_did_data( + n_groups=20, n_periods=8, pattern="mixed_single_switch", seed=3 + ) + for g in df["group"].unique(): + grp = df[df["group"] == g].sort_values("period")["treatment"].to_numpy() + n_switches = (grp[1:] != grp[:-1]).sum() + assert n_switches == 1 + + +class TestRandomPattern: + """Random flip pattern — produces multi-switch groups for n_periods >= 4.""" + + def test_p_switch_zero_means_no_switches(self): + df = generate_reversible_did_data( + n_groups=20, n_periods=6, pattern="random", p_switch=0.0, seed=1 + ) + # With p_switch=0, every group keeps its initial state + for g in df["group"].unique(): + grp = df[df["group"] == g].sort_values("period")["treatment"].to_numpy() + assert len(set(grp)) == 1 + + def test_random_produces_some_switches(self): + df = generate_reversible_did_data( + n_groups=100, n_periods=8, pattern="random", p_switch=0.4, seed=42 + ) + # With p_switch=0.4 and n_periods=8, the expected number of multi-switch + # groups is high. We just assert at least one switch happens somewhere. + total_switches = 0 + for g in df["group"].unique(): + grp = df[df["group"] == g].sort_values("period")["treatment"].to_numpy() + total_switches += (grp[1:] != grp[:-1]).sum() + assert total_switches > 0 + + +class TestCyclesPattern: + """Deterministic on/off cycles — guaranteed multi-switch.""" + + def test_cycle_length_2_exact_sequence(self): + df = generate_reversible_did_data( + n_groups=4, n_periods=6, pattern="cycles", cycle_length=2, seed=1 + ) + # First two groups: phase = (t // 2) % 2 → [0,0,1,1,0,0] + # Last two groups: opposite → [1,1,0,0,1,1] + first = df[df["group"] == 0].sort_values("period")["treatment"].tolist() + last = df[df["group"] == 3].sort_values("period")["treatment"].tolist() + assert first == [0, 0, 1, 1, 0, 0] + assert last == [1, 1, 0, 0, 1, 1] + + def test_cycles_produces_multi_switch_groups(self): + df = generate_reversible_did_data( + n_groups=10, n_periods=8, pattern="cycles", cycle_length=2, seed=1 + ) + for g in df["group"].unique(): + grp = df[df["group"] == g].sort_values("period")["treatment"].to_numpy() + n_switches = (grp[1:] != grp[:-1]).sum() + # cycles always produces > 1 switch when n_periods > 2 * cycle_length + assert n_switches >= 2 + + +class TestMarketingPattern: + """Seasonal '2 on, 1 off' pattern — guaranteed multi-switch.""" + + def test_marketing_pattern_exact_sequence(self): + df = generate_reversible_did_data(n_groups=5, n_periods=9, pattern="marketing", seed=1) + # Pattern: t % 3 != 2 → on, else → off + # → [1, 1, 0, 1, 1, 0, 1, 1, 0] + for g in df["group"].unique(): + grp = df[df["group"] == g].sort_values("period")["treatment"].tolist() + assert grp == [1, 1, 0, 1, 1, 0, 1, 1, 0] + + def test_marketing_all_groups_identical(self): + df = generate_reversible_did_data(n_groups=8, n_periods=6, pattern="marketing", seed=1) + first = df[df["group"] == 0].sort_values("period")["treatment"].tolist() + for g in df["group"].unique(): + grp = df[df["group"] == g].sort_values("period")["treatment"].tolist() + assert grp == first + + +# ============================================================================= +# Reproducibility +# ============================================================================= + + +class TestSeedReproducibility: + def test_same_seed_same_data_random(self): + df1 = generate_reversible_did_data(n_groups=20, n_periods=6, pattern="random", seed=42) + df2 = generate_reversible_did_data(n_groups=20, n_periods=6, pattern="random", seed=42) + pd.testing.assert_frame_equal(df1, df2) + + def test_same_seed_same_data_single_switch(self): + df1 = generate_reversible_did_data(n_groups=15, n_periods=5, seed=7) + df2 = generate_reversible_did_data(n_groups=15, n_periods=5, seed=7) + pd.testing.assert_frame_equal(df1, df2) + + def test_different_seeds_different_data(self): + df1 = generate_reversible_did_data(n_groups=20, n_periods=6, seed=1) + df2 = generate_reversible_did_data(n_groups=20, n_periods=6, seed=2) + # The treatment matrices should differ + assert not df1["treatment"].equals(df2["treatment"]) + + +# ============================================================================= +# True effect column (ground truth for downstream tests) +# ============================================================================= + + +class TestTrueEffectColumn: + def test_zero_on_untreated_cells(self): + df = generate_reversible_did_data(n_groups=20, n_periods=6, seed=1) + untreated = df[df["treatment"] == 0] + assert (untreated["true_effect"] == 0.0).all() + + def test_constant_treatment_effect_homogeneous(self): + df = generate_reversible_did_data( + n_groups=20, + n_periods=6, + treatment_effect=3.5, + heterogeneous_effects=False, + seed=1, + ) + treated = df[df["treatment"] == 1] + assert (treated["true_effect"] == 3.5).all() + + def test_heterogeneous_effects_vary(self): + df = generate_reversible_did_data( + n_groups=50, + n_periods=6, + treatment_effect=2.0, + heterogeneous_effects=True, + effect_sd=0.5, + seed=42, + ) + treated = df[df["treatment"] == 1] + # With heterogeneous effects, the std should be approximately effect_sd + assert treated["true_effect"].std() > 0.1 + + +# ============================================================================= +# Switcher type classification +# ============================================================================= + + +class TestSwitcherTypeColumn: + def test_period_zero_is_initial(self): + df = generate_reversible_did_data(n_groups=10, n_periods=5, seed=1) + period_zero = df[df["period"] == 0] + assert (period_zero["switcher_type"] == "initial").all() + + def test_d_lag_nan_at_period_zero(self): + df = generate_reversible_did_data(n_groups=10, n_periods=5, seed=1) + period_zero = df[df["period"] == 0] + assert period_zero["d_lag"].isna().all() + + def test_d_lag_finite_after_period_zero(self): + df = generate_reversible_did_data(n_groups=10, n_periods=5, seed=1) + non_zero = df[df["period"] > 0] + assert non_zero["d_lag"].notna().all() + + def test_switcher_type_matches_lag_diff_joiners_only(self): + df = generate_reversible_did_data(n_groups=10, n_periods=5, pattern="joiners_only", seed=1) + # In joiners_only, period 0 is "initial", and within each group + # there is exactly one "joiner" cell (the switch) followed by + # "stable_1" cells. The remaining post-period-0 cells before + # the switch are "stable_0". + for g in df["group"].unique(): + grp = df[df["group"] == g].sort_values("period") + types = grp["switcher_type"].tolist() + assert types[0] == "initial" + joiner_count = sum(1 for t in types if t == "joiner") + leaver_count = sum(1 for t in types if t == "leaver") + assert joiner_count <= 1 # at most one joiner cell per group + assert leaver_count == 0 # never a leaver in joiners_only + + def test_switcher_type_classification_explicit(self): + """Build a known panel and verify each cell's switcher_type.""" + # Use single_switch with seed to control the panel + # Check specific (treatment, d_lag) combinations are classified correctly. + df = generate_reversible_did_data(n_groups=20, n_periods=6, seed=42) + # Period 0 cells must be "initial" with NaN d_lag + p0 = df[df["period"] == 0] + assert (p0["switcher_type"] == "initial").all() + # Period > 0 cells: classification matches (treatment, d_lag) + post = df[df["period"] > 0] + joiners = post[(post["d_lag"] == 0) & (post["treatment"] == 1)] + leavers = post[(post["d_lag"] == 1) & (post["treatment"] == 0)] + stable_0 = post[(post["d_lag"] == 0) & (post["treatment"] == 0)] + stable_1 = post[(post["d_lag"] == 1) & (post["treatment"] == 1)] + assert (joiners["switcher_type"] == "joiner").all() + assert (leavers["switcher_type"] == "leaver").all() + assert (stable_0["switcher_type"] == "stable_0").all() + assert (stable_1["switcher_type"] == "stable_1").all() + + +# ============================================================================= +# Validation errors +# ============================================================================= + + +class TestValidationErrors: + def test_invalid_pattern_raises(self): + with pytest.raises(ValueError, match="pattern must be one of"): + generate_reversible_did_data(pattern="bogus", seed=1) + + def test_n_groups_zero_raises(self): + with pytest.raises(ValueError, match="n_groups must be positive"): + generate_reversible_did_data(n_groups=0, seed=1) + + def test_n_periods_one_raises(self): + with pytest.raises(ValueError, match="n_periods must be at least 2"): + generate_reversible_did_data(n_groups=10, n_periods=1, seed=1) + + def test_initial_treat_frac_out_of_range_raises(self): + with pytest.raises(ValueError, match="initial_treat_frac must be in"): + generate_reversible_did_data(initial_treat_frac=1.5, seed=1) + + def test_p_switch_out_of_range_raises(self): + with pytest.raises(ValueError, match="p_switch must be in"): + generate_reversible_did_data(p_switch=-0.1, seed=1) + + def test_negative_cycle_length_raises(self): + with pytest.raises(ValueError, match="cycle_length must be positive"): + generate_reversible_did_data(cycle_length=0, seed=1) + + def test_negative_noise_sd_raises(self): + with pytest.raises(ValueError, match="noise_sd must be non-negative"): + generate_reversible_did_data(noise_sd=-1.0, seed=1)