igerber · gorgeousfish · Jun 30, 2026 · Jul 19, 2026 · Jul 19, 2026 · Jul 19, 2026
diff --git a/CHANGELOG.md b/CHANGELOG.md
@@ -34,6 +34,27 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
   `tests/test_docs_ia.py`.
 
 ### Fixed
+- **`LWDiD` review-round fixes** (staggered contract and inference tightenings):
+  - Staggered classical/HC SEs now come from the joint influence function
+    across cohort-time cells (the LW 2026 eq. 7.19 pooled-regression basis),
+    accounting for correlation among cohort effects that share controls
+    instead of assuming independence.
+  - All-eventually-treated panels under `control_group='not_yet_treated'`
+    raise `ValueError` instead of silently truncating the sample; staggered
+    `covariates` must be unit-constant, time-varying columns raise
+    `ValueError`.
+  - Randomization inference counts ties as extreme (`>=`), so an all-tie
+    permutation distribution yields p = 1.0 rather than 0.
+  - `sensitivity_analysis` gains a `not_estimable` robustness level (with a
+    warning) when the ratio cannot be computed, instead of mislabeling it.
+  - `recommend_transformation` validates the `cohort` column (unknown names
+    raise `ValueError` instead of silently degrading to common timing) and
+    actually uses it for staggered diagnostics.
+  - `to_dict()` output is fully JSON-native, including datetime/Period
+    cohort and time labels (ISO-8601 / period strings, NaT -> None).
+  - Staggered fits accept datetime64 and Period time scales; cluster
+    variable equal to the unit column no longer raises a spurious
+    column-lookup error.
 - **`docs/r_comparison.rst` migration tips named a nonexistent results field**
   (`.ci`); the canonical accessor is `.conf_int`. The `aggte()` comparison
   comment also claimed aggregation is requested at fit time, which stopped being
@@ -1046,6 +1067,10 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
   point estimates, SEs, and t-statistics never move. Results objects echo
   `df_convention`, and `SunAbraham`/`StackedDiD` gain `inference_df` (the
   overall-ATT df actually used; None under bootstrap overrides).
+- **`LWDiD` (Lee & Wooldridge 2025, 2026 rolling-transformation DiD).** Unit-specific
+  demean/detrend converts panel data to cross-sectional transformed outcomes;
+  supports staggered adoption with never-treated / not-yet-treated controls,
+  RA/IPW/IPWRA estimation, and cluster-robust inference. Alias `LW`.
 - **Stata parity arm for ETWFE and Callaway-Sant'Anna ATT(g,t) (`jwdid` / `csdid`).**
   `benchmarks/stata/generate_etwfe_cs_golden.do` anchors both staggered
   estimators against their canonical Stata implementations on the genuine

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 via `method="qdid"`; 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
@@ -160,6 +160,8 @@
 )
 from diff_diff.lpdid import LPDiD
 from diff_diff.lpdid_results import LPDiDResults
+from diff_diff.lwdid import LWDiD
+from diff_diff.lwdid_results import LWDiDResults
 from diff_diff.mmm import (
     MeridianROIPrior,
     to_meridian_roi_prior,
@@ -333,6 +335,7 @@
 CiC = ChangesInChanges
 RDD = RegressionDiscontinuity
 SCM = SyntheticControl
+LW = LWDiD
 
 # Alias diet (rows M-132..M-134, mechanism M-135): CDiD / Gardner /
 # Stacked are deprecated in 3.9 and removed in 4.0. They deliberately
@@ -454,6 +457,10 @@ def __getattr__(name: str) -> _Any:
     # LPDiD (Local Projections DiD)
     "LPDiD",
     "LPDiDResults",
+    # LWDiD (Lee & Wooldridge rolling transformation DiD)
+    "LWDiD",
+    "LWDiDResults",
+    "LW",
     # Visualization
     "plot_bacon",
     "plot_event_study",

diff --git a/diff_diff/guides/llms.txt b/diff_diff/guides/llms.txt
@@ -80,6 +80,7 @@ The site is organized into 5 sections, each with a landing page:
 - [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): **Deprecated 3.9, removed 4.0 - use `ChangesInChanges(method="qdid")`.** 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