igerber · gorgeousfish · Jun 30, 2026
diff --git a/CHANGELOG.md b/CHANGELOG.md
@@ -8,6 +8,10 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 ## [Unreleased]
 
 ### Added
+- **`LWDiD` (Lee & Wooldridge 2025, 2026 rolling-transformation DiD).** Unit-specific
+  demean/detrend converts panel to cross-section; supports staggered adoption with
+  never-treated / not-yet-treated control groups, RA/IPW/IPWRA estimation, and
+  cluster-robust inference. Alias `LW`.
 - **`RegressionDiscontinuity` - sharp AND fuzzy regression discontinuity estimation
   with robust bias-corrected inference (alias `RDD`).** Local-polynomial RD per
   Calonico, Cattaneo & Titiunik (2014), parity-targeting R `rdrobust` 4.0.0

diff --git a/README.md b/README.md
@@ -120,6 +120,7 @@ Full guide: `diff_diff.get_llm_guide("practitioner")`.
 - [WooldridgeDiD](https://diff-diff.readthedocs.io/en/stable/api/wooldridge_etwfe.html) - Wooldridge (2023, 2025) ETWFE: saturated OLS, logit/Poisson QMLE (ASF-based ATT). Alias `ETWFE`.
 - [LPDiD](https://diff-diff.readthedocs.io/en/stable/api/lpdid.html) - Dube, Girardi, Jorda & Taylor (2025) Local Projections DiD: per-horizon long-difference event study on clean controls (no negative weighting), variance- or equally-weighted ATT, for absorbing or non-absorbing (reversible) treatment
 - [ChangesInChanges](https://diff-diff.readthedocs.io/en/stable/api/changes_in_changes.html) - Athey & Imbens (2006) nonlinear/distributional DiD for the 2x2 design: full counterfactual distribution and quantile treatment effects via CDF transformation, plus the QDiD comparison estimator; bootstrap inference; R qte parity. Alias `CiC`
+- [LWDiD](https://diff-diff.readthedocs.io/en/stable/api/lwdid.html) - Lee & Wooldridge (2025, 2026) rolling-transformation DiD: unit-specific demean/detrend converts panel to cross-section, staggered adoption, RA/IPW/IPWRA estimation. Alias `LW`.
 - [BaconDecomposition](https://diff-diff.readthedocs.io/en/stable/api/bacon.html) - Goodman-Bacon (2021) decomposition for diagnosing TWFE bias in staggered settings
 
 ## Diagnostics & Sensitivity

diff --git a/diff_diff/__init__.py b/diff_diff/__init__.py
@@ -154,6 +154,53 @@
 )
 from diff_diff.lpdid import LPDiD
 from diff_diff.lpdid_results import LPDiDResults
+from diff_diff.lwdid import LWDiD, is_never_treated, lwdid, validate_staggered_data
+from diff_diff.lwdid_clustering import (
+    ClusteringDiagnostics,
+    ClusteringRecommendation,
+    diagnose_clustering,
+    diagnose_clustering_from_data,
+    recommend_clustering_level,
+)
+from diff_diff.lwdid_exceptions import (
+    BootstrapConvergenceError,
+    DiagnosticError,
+    DiagnosticWarning,
+    InsufficientPrePeriodsError,
+    LWDIDError,
+    LWDIDInferenceError,
+    LWDIDWarning,
+    NumericalWarning,
+    RandomizationError,
+    RandomizationWarning,
+    SensitivityWarning,
+    VisualizationError,
+    VisualizationWarning,
+)
+from diff_diff.lwdid_randomization import RandomizationResult, randomization_inference
+from diff_diff.lwdid_results import LWDiDResults
+from diff_diff.lwdid_sensitivity import (
+    SensitivityResult,
+    robustness_pre_periods,
+    sensitivity_analysis,
+    sensitivity_no_anticipation,
+)
+from diff_diff.lwdid_trend_diagnostics import (
+    ParallelTrendsTestResult,
+    diagnose_heterogeneous_trends,
+    recommend_transformation,
+    test_parallel_trends,
+)
+from diff_diff.lwdid_visualization import (
+    plot_bootstrap_distribution,
+    plot_cohort_trends,
+)
+from diff_diff.lwdid_visualization import plot_event_study as plot_lwdid_event_study
+from diff_diff.lwdid_visualization import plot_sensitivity as plot_lwdid_sensitivity
+from diff_diff.lwdid_wild_bootstrap import (
+    WildClusterBootstrapResult,
+    wild_cluster_bootstrap,
+)
 from diff_diff.power import (
     PowerAnalysis,
     PowerResults,
@@ -315,6 +362,7 @@
 HAD = HeterogeneousAdoptionDiD
 CiC = ChangesInChanges
 RDD = RegressionDiscontinuity
+LW = LWDiD
 
 __version__ = "3.7.0"
 __all__ = [
@@ -406,6 +454,46 @@
     # LPDiD (Local Projections DiD)
     "LPDiD",
     "LPDiDResults",
+    # LWDiD (Lee & Wooldridge rolling transformation DiD)
+    "LWDiD",
+    "LWDiDResults",
+    "LW",
+    "wild_cluster_bootstrap",
+    "WildClusterBootstrapResult",
+    "randomization_inference",
+    "RandomizationResult",
+    "test_parallel_trends",
+    "diagnose_heterogeneous_trends",
+    "recommend_transformation",
+    "ParallelTrendsTestResult",
+    "sensitivity_analysis",
+    "robustness_pre_periods",
+    "sensitivity_no_anticipation",
+    "SensitivityResult",
+    "lwdid",
+    "plot_cohort_trends",
+    "plot_lwdid_event_study",
+    "plot_lwdid_sensitivity",
+    "plot_bootstrap_distribution",
+    # LWDiD exceptions
+    "LWDIDError",
+    "LWDIDWarning",
+    "LWDIDInferenceError",
+    "RandomizationError",
+    "DiagnosticError",
+    "NumericalWarning",
+    "DiagnosticWarning",
+    "SensitivityWarning",
+    "VisualizationError",
+    # LWDiD clustering diagnostics
+    "diagnose_clustering",
+    "diagnose_clustering_from_data",
+    "recommend_clustering_level",
+    "ClusteringDiagnostics",
+    "ClusteringRecommendation",
+    # LWDiD utility functions
+    "validate_staggered_data",
+    "is_never_treated",
     # Visualization
     "plot_bacon",
     "plot_event_study",

diff --git a/diff_diff/guides/llms.txt b/diff_diff/guides/llms.txt
@@ -73,6 +73,7 @@ Full practitioner guide: call `diff_diff.get_llm_guide("practitioner")`
 - [LPDiD](https://diff-diff.readthedocs.io/en/stable/api/lpdid.html): Dube, Girardi, Jorda & Taylor (2025) Local Projections DiD: per-horizon long-difference event study on clean controls (no negative weighting); variance- or equally-weighted ATT, premean differencing, pooled pre/post, fast. Absorbing by default; non-absorbing (reversible) treatment via `non_absorbing="first_entry"` (Eq. 12) or `"effect_stabilization"` (Eq. 13, window `L`). Complex-survey designs (pweight + stratified-PSU TSL SEs) on the default path via `fit(survey_design=...)`.
 - [ChangesInChanges](https://diff-diff.readthedocs.io/en/stable/api/changes_in_changes.html): Athey & Imbens (2006) nonlinear/distributional DiD for the 2x2 design: recovers the treated group's full counterfactual outcome distribution and quantile treatment effects (ATT + QTE grid) via the CDF transformation `F_10(F_00^{-1}(F_01(y)))`; invariant to monotone outcome transformations (unconditional fits; the covariate QR branch is not); bootstrap inference (panel or repeated cross-section resampling); point parity with R `qte::CiC()`, including its covariate branch (`covariates=` -> per-cell linear quantile regression, Melly-Santangelo-style conditional CiC). Continuous outcomes, numeric covariates. Alias `CiC`.
 - [QDiD](https://diff-diff.readthedocs.io/en/stable/api/changes_in_changes.html): Athey & Imbens (2006) quantile DiD comparison estimator (additive quantile-by-quantile DiD, matching R `qte::QDiD()` including its covariate branch via `covariates=`); same bootstrap machinery as ChangesInChanges. The paper recommends CiC over QDiD (scale-dependent model with testable restrictions; a non-monotonicity warning fires when violated - unconditional fits only, the covariate-path counterfactual quantile curve is monotone by construction).
+- [LWDiD](https://diff-diff.readthedocs.io/en/stable/api/lwdid.html): Lee & Wooldridge (2025, 2026) rolling-transformation DiD — unit-specific demean/detrend converts panel to cross-section, supports staggered adoption with flexible control groups and estimation (RA/IPW/IPWRA). Alias: LW
 - [BaconDecomposition](https://diff-diff.readthedocs.io/en/stable/api/bacon.html): Goodman-Bacon (2021) decomposition for diagnosing TWFE bias in staggered settings
 
 ## Diagnostics and Sensitivity Analysis