igerber · igerber · Jun 29, 2026 · Jun 29, 2026 · Jun 29, 2026
diff --git a/CHANGELOG.md b/CHANGELOG.md
@@ -8,6 +8,18 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 ## [Unreleased]
 
 ### Added
+- **`LPDiD` non-absorbing (reversible) treatment** (Dube, Girardi, Jordà & Taylor 2025,
+  Section 4.2). New `non_absorbing` parameter: `"first_entry"` (Eq. 12 — the effect of
+  entering treatment for the first time and staying treated) and `"effect_stabilization"`
+  (Eq. 13, with `stabilization_window=L` — units whose treatment has been stable for at least
+  `L` periods serve as clean controls, so estimation is feasible with few or no never-treated
+  units). The default `non_absorbing=None` is unchanged (absorbing path; still rejects
+  non-absorbing input, bit-for-bit identical results). Both modes implement the entry-effect
+  estimands with mode-aware clean-sample masks, a documented "untreated before the first
+  observed period" boundary convention, and a gap-free-panel requirement; the Appendix-C
+  exit-event dynamics, R-package parity (PR-C2), and survey-design support remain follow-ups.
+  Pure-Python validation covers the absorbing reduction, the re-entry mechanism, pre-trend
+  placebos, non-negative weighting, stabilized-control admission, and DGP recovery.
 - **`LPDiD` R-parity validation (absorbing).** `tests/test_methodology_lpdid.py` pins the
   estimator against the method authors' own R recipes (`danielegirardi/lpdid` event-study /
   reweight / premean / pooled `fixest::feols` specifications) with an `alexCardazzi/lpdid`

diff --git a/README.md b/README.md
@@ -117,7 +117,7 @@ Full guide: `diff_diff.get_llm_guide("practitioner")`.
 - [TROP](https://diff-diff.readthedocs.io/en/stable/api/trop.html) - Triply Robust Panel estimator (Athey et al. 2025) with nuclear norm factor adjustment
 - [StaggeredTripleDifference](https://diff-diff.readthedocs.io/en/stable/api/staggered.html#staggeredtripledifference) - Ortiz-Villavicencio & Sant'Anna (2025) staggered DDD with group-time ATT
 - [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 treatment
+- [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
 - [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/TODO.md b/TODO.md
@@ -94,6 +94,8 @@ Not currently actionable. Retained for provenance + AI-review deviation-document
 | `SpilloverDiD(survey_design=...)` replicate-weight variance (BRR/Fay/JK1/JKn/SDR): Wave E.1 ships Taylor-linearization only. Per Gerber (2026) Appendix A the IF-reweighting shortcut does NOT apply to TwoStageDiD-class estimators (`gamma_hat` is weight-sensitive); correct support needs per-replicate full re-fit of both stages. | `spillover.py`, `survey.py::compute_replicate_refit_variance` | follow-up | Low |
 | `SpilloverDiD(vcov_type="conley", conley_lag_cutoff>0, survey_design=...)` no-effective-PSU serial Bartlett HAC: weights-only / strata-only designs without a cluster fallback raise `NotImplementedError` (each pseudo-PSU appears in one period, so the serial cross-period loop contributes zero). Needs a unit-level serial fallback derivation or routing through `conley_unit` with documented IF-allocator asymmetry. | `spillover.py`, `two_stage.py::_compute_stratified_serial_bartlett_meat` | Wave E.2 tail | Low |
 | `SpilloverDiD` data-driven `d_bar` selection (Butts 2021b / 2023 JUE Insight cross-validation). | `spillover.py` | follow-up | Low |
+| **`LPDiD` non-absorbing exit-event dynamics** (Dube et al. 2025 online Appendix C `eta_h^{g,n}`): the shipped `non_absorbing` modes estimate the **entry-effect** estimands (Eq. 12/13) only; separate dynamic event-studies for treatment switch-*offs* are not implemented. Needs the exit-event clean-sample derivation + estimand contract. | `lpdid.py`, REGISTRY | PR-C follow-up | Low |
+| **`LPDiD` non-absorbing interior-gap support**: non-absorbing modes require a gap-free panel within each unit's observed span and raise on interior time gaps (the `[t-L, t+h]` window conditions can't be verified across a gap). The absorbing path already reindexes interior gaps to the calendar grid; extending that fail-closed handling (per-window gap masking) to non-absorbing is deferred. | `lpdid.py::_prepare_panel` | PR-C follow-up | Low |
 
 ### Needs external reference (R / Stata / Julia)
 
@@ -116,6 +118,7 @@ exists but parity can't be verified without a local toolchain.
 | **`bias_corrected_local_linear` (lprobust) Phase-1c follow-ups:** extend golden parity to `kernel ∈ {triangular, uniform}` (epa-only today); expose `vce ∈ {hc0,hc1,hc2,hc3}` on the public wrapper once R goldens exist (port supports all four; needs a per-mode generator + a hc2/hc3 q-fit-leverage decision); clustered-DGP auto-bandwidth parity is **blocked upstream** on an nprobust singleton-cluster bug in `lpbwselect.mse.dpi` (Phase-1c DGP 4 uses manual `h=b=0.3`). | `_nprobust_port.py`, `local_linear.py`, `generate_nprobust_lprobust_golden.R` | Phase 1c | Low-Med |
 | `HeterogeneousAdoptionDiD` Stute-family Stata-bridge parity: no public R `Stutetest` package exists; would add `benchmarks/stata/generate_stute_golden.do` + a Stata dependency. | `benchmarks/stata/`, `tests/test_stute_test_parity.py` | follow-up | Low |
 | **`LPDiD` regression-adjustment SE — no runnable R reference.** The RA influence-function cluster SE is canonically Stata `teffects ra ... atet vce(cluster)` only; no R package computes it (`alexCardazzi/lpdid` does direct covariate inclusion, not RA). Today the RA *point* is R-anchored (~1e-12), the SE is pinned + MC-coverage-validated (`coverage_lpdid_ra.py`). Follow-up: contribute the RA path to `alexCardazzi/lpdid` so a runnable R RA reference exists — only a *trusted* anchor once cross-checked vs Stata `teffects` (else circular). | `tests/test_methodology_lpdid.py`, `benchmarks/python/coverage_lpdid_ra.py` | #B2 follow-up | Low |
+| **`LPDiD` non-absorbing R-parity (PR-C2).** The shipped non-absorbing modes (`first_entry` Eq. 12 / `effect_stabilization` Eq. 13) are validated by pure-Python tests (absorbing reduction, re-entry mechanism, placebo, non-negative weighting, DGP recovery) but not yet pinned to `alexCardazzi/lpdid`'s `nonabsorbing` / `nonabsorbing_lag` branches. PR-C2: smoke-gate the option mapping (incl. the equal-weight reweight and the pre-observation boundary convention), then extend `generate_lpdid_golden.R` + `test_methodology_lpdid.py` with non-absorbing variants. | `benchmarks/R/generate_lpdid_golden.R`, `tests/test_methodology_lpdid.py` | PR-C2 | Medium |
 | `HeterogeneousAdoptionDiD` Phase-3 R-parity: ships coverage-rate validation on synthetic DGPs, not tight point parity vs `chaisemartin::stute_test` / `yatchew_test` (needs bootstrap-seed-semantics + `B` alignment across numpy/R). | `tests/test_had_pretests.py` | Phase 3 | Low |
 
 ### Parked — pending user demand / out of scope

diff --git a/diff_diff/guides/llms-full.txt b/diff_diff/guides/llms-full.txt
@@ -897,7 +897,7 @@ results.print_summary()
 
 ### LPDiD
 
-Local Projections DiD (Dube, Girardi, Jorda & Taylor 2025). Estimates a separate OLS at each event-time horizon of a long difference (`y_{i,t+h} - y_{i,t-1}`) on the treatment-switch indicator plus calendar-time fixed effects (no unit FE), restricted to a flexible "clean control" sample of newly-treated and not-yet-treated units. Excluding already-treated units from the control group removes the negative-weighting bias of naive TWFE, so the default (variance-weighted) estimand has strictly non-negative weights. `reweight=True` yields the equally-weighted ATT (numerically equivalent to Callaway-Sant'Anna); covariates then enter via regression adjustment. Standard errors on the default/weighted path are cluster-robust at the unit level (the paper specifies no SE; matches Stata `lpdid` `vce(cluster unit)`); the regression-adjustment covariate path (`reweight=True`) instead reports an influence-function cluster variance (ImputationDiD/BJS family). Scope: binary, absorbing treatment (rejects panels where treatment turns off).
+Local Projections DiD (Dube, Girardi, Jorda & Taylor 2025). Estimates a separate OLS at each event-time horizon of a long difference (`y_{i,t+h} - y_{i,t-1}`) on the treatment-switch indicator plus calendar-time fixed effects (no unit FE), restricted to a flexible "clean control" sample of newly-treated and not-yet-treated units. Excluding already-treated units from the control group removes the negative-weighting bias of naive TWFE, so the default (variance-weighted) estimand has strictly non-negative weights. `reweight=True` yields the equally-weighted ATT (numerically equivalent to Callaway-Sant'Anna); covariates then enter via regression adjustment. Standard errors on the default/weighted path are cluster-robust at the unit level (the paper specifies no SE; matches Stata `lpdid` `vce(cluster unit)`); the regression-adjustment covariate path (`reweight=True`) instead reports an influence-function cluster variance (ImputationDiD/BJS family). Scope: binary treatment; absorbing by default (rejects panels where treatment turns off), with non-absorbing (reversible) treatment available via `non_absorbing` - `"first_entry"` (Dube et al. Eq. 12, the effect of entering for the first time and staying treated) or `"effect_stabilization"` (Eq. 13, requires `stabilization_window=L`; lets units whose treatment has been stable for at least `L` periods act as clean controls, so estimation is feasible with few/no never-treated units). Non-absorbing modes require a gap-free panel within each unit's observed span.
 
 ```python
 LPDiD(
@@ -910,6 +910,8 @@ LPDiD(
     alpha: float = 0.05,
     cluster: str | None = None,              # Cluster column for cluster-robust SEs; defaults to the unit identifier
     rank_deficient_action: str = "warn",     # "warn", "error", or "silent"
+    non_absorbing: str | None = None,        # None=absorbing; "first_entry" (Eq. 12); "effect_stabilization" (Eq. 13)
+    stabilization_window: int | None = None, # The paper's L; required when non_absorbing="effect_stabilization"
 )
 ```
 
@@ -921,7 +923,7 @@ lpdid.fit(
     outcome: str,
     unit: str,
     time: str,
-    treatment: str,                    # Binary, absorbing treatment indicator (0/1)
+    treatment: str,                    # Binary treatment indicator (0/1); absorbing unless non_absorbing is set
     covariates: list[str] = None,      # Direct inclusion (reweight=False) or regression adjustment (reweight=True)
     ylags: int = 0,                    # Lagged-outcome controls
     dylags: int = 0,                   # Lagged first-difference controls

diff --git a/diff_diff/guides/llms.txt b/diff_diff/guides/llms.txt
@@ -69,7 +69,7 @@ Full practitioner guide: call `diff_diff.get_llm_guide("practitioner")`
 - [TROP](https://diff-diff.readthedocs.io/en/stable/api/trop.html): Triply Robust Panel estimator (Athey et al. 2025) with nuclear norm factor adjustment
 - [StaggeredTripleDifference](https://diff-diff.readthedocs.io/en/stable/api/staggered.html#staggeredtripledifference): Ortiz-Villavicencio & Sant'Anna (2025) staggered DDD with group-time ATT
 - [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, premean differencing, pooled pre/post, fast. Absorbing treatment.
+- [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`).
 - [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