Address PR #372 R6 review (1 P2): backfill HeterogeneousAdoptionDiD across all canonical surfaces

P2 - HeterogeneousAdoptionDiD shipped (Phases 1a-3 + 3-followup + 4.5 A/B/C0
merged via PRs #366/#367) but missing from the new authoritative documentation
surfaces this PR establishes
- README.md ## Estimators catalog: add one-line entry after ContinuousDiD
  (de Chaisemartin, Ciccia, D'Haultfoeuille & Knau 2026; alias HAD).
- diff_diff/guides/llms.txt ## Estimators: add matching one-liner.
- .claude/commands/docs-check.md required-estimators table: add HAD row
  pointing at had.rst as the API target.
- docs/references.rst: add new "Heterogeneous Adoption (No-Untreated
  Designs)" sub-section with the de Chaisemartin et al. (2026)
  arXiv:2405.04465v6 citation.
- docs/api/index.rst: add HeterogeneousAdoptionDiD to estimators
  autosummary; add HeterogeneousAdoptionDiDResults +
  HeterogeneousAdoptionDiDEventStudyResults to results autosummary.
- docs/api/had.rst (NEW): autoclass page for the three classes with a
  brief intro and a "When to use HAD" note pointing at sibling estimators
  (ContinuousDiD for never-treated controls, dCDH for binary reversible).
- docs/doc-deps.yaml: add diff_diff/had.py + diff_diff/had_pretests.py
  source mappings (REGISTRY methodology, had.rst api_reference, README
  catalog, references.rst, llms.txt). The llms-full.txt mapping is
  intentionally omitted with a comment - that section is deferred to the
  Phase 5 follow-up tracked in TODO.md.

TODO.md: narrow the Phase 5 entry from "llms.txt updates" to "llms-full.txt
HeterogeneousAdoptionDiD section" since the catalog one-liner, API page,
and bibliography landed here. practitioner_next_steps() integration and
tutorial notebook remain deferred.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

Author: igerber

.claude/commands/docs-check.md

@@ -48,6 +48,7 @@ The following estimators/features MUST have documentation:
 | EfficientDiD | "EfficientDiD" | "EfficientDiD" | efficient_did.rst | "Multi-Period and Staggered" |
 | StackedDiD | "StackedDiD" | "StackedDiD" | stacked_did.rst | "Multi-Period and Staggered" |
 | ContinuousDiD | "ContinuousDiD" | "ContinuousDiD" | continuous_did.rst | "Multi-Period and Staggered" |
+| HeterogeneousAdoptionDiD | "HeterogeneousAdoptionDiD" | "HeterogeneousAdoptionDiD" | had.rst | "Heterogeneous Adoption (No-Untreated Designs)" |
 | TripleDifference | "TripleDifference" | "TripleDifference" | triple_diff.rst | "Triple Difference" |
 | StaggeredTripleDifference | "StaggeredTripleDifference" | "StaggeredTripleDifference" | staggered.rst | "Triple Difference" |
 | WooldridgeDiD | "WooldridgeDiD" | "WooldridgeDiD" | wooldridge_etwfe.rst | "Multi-Period and Staggered" |

README.md

@@ -110,6 +110,7 @@ Full guide: `diff_diff.get_llm_guide("practitioner")`.
 - [SyntheticDiD](https://diff-diff.readthedocs.io/en/stable/api/estimators.html) - Synthetic DiD combining standard DiD and synthetic control for few treated units
 - [TripleDifference](https://diff-diff.readthedocs.io/en/stable/api/triple_diff.html) - triple difference (DDD) estimator for designs requiring two criteria for treatment eligibility
 - [ContinuousDiD](https://diff-diff.readthedocs.io/en/stable/api/continuous_did.html) - Callaway, Goodman-Bacon & Sant'Anna (2024) continuous treatment DiD with dose-response curves
+- [HeterogeneousAdoptionDiD](https://diff-diff.readthedocs.io/en/stable/api/had.html) - de Chaisemartin, Ciccia, D'Haultfœuille & Knau (2026) for **no-untreated-unit panels** where every unit receives a strictly positive heterogeneous dose; local-linear estimator targeting the Weighted Average Slope (WAS) at the support boundary, with multi-period event-study extension. Alias `HAD`.
 - [StackedDiD](https://diff-diff.readthedocs.io/en/stable/api/stacked_did.html) - Wing, Freedman & Hollingsworth (2024) stacked DiD with Q-weights and sub-experiments
 - [EfficientDiD](https://diff-diff.readthedocs.io/en/stable/api/efficient_did.html) - Chen, Sant'Anna & Xie (2025) efficient DiD with optimal weighting for tighter SEs
 - [TROP](https://diff-diff.readthedocs.io/en/stable/api/trop.html) - Triply Robust Panel estimator (Athey et al. 2025) with nuclear norm factor adjustment

TODO.md

@@ -105,7 +105,7 @@ Deferred items from PR reviews that were not addressed before merge.
 | `HeterogeneousAdoptionDiD` Phase 3 R-parity: Phase 3 ships coverage-rate validation on synthetic DGPs (not tight point parity against `chaisemartin::stute_test` / `yatchew_test`). Tight numerical parity requires aligning bootstrap seed semantics and `B` across numpy/R and is deferred. | `tests/test_had_pretests.py` | Phase 3 | Low |
 | `HeterogeneousAdoptionDiD` Phase 3 nprobust bandwidth for Stute: some Stute variants on continuous regressors use nprobust-style optimal bandwidth selection. Phase 3 uses OLS residuals from a 2-parameter linear fit (no bandwidth selection). nprobust integration is a future enhancement; not in paper scope. | `diff_diff/had_pretests.py::stute_test` | Phase 3 | Low |
 | `HeterogeneousAdoptionDiD` Phase 4: Pierce-Schott (2016) replication harness; reproduce paper Figure 2 values and Table 1 coverage rates. | `benchmarks/`, `tests/` | Phase 2a | Low |
-| `HeterogeneousAdoptionDiD` Phase 5: `practitioner_next_steps()` integration, tutorial notebook, and `llms.txt` updates (preserving UTF-8 fingerprint). | `diff_diff/practitioner.py`, `tutorials/`, `diff_diff/guides/` | Phase 2a | Low |
+| `HeterogeneousAdoptionDiD` Phase 5: `practitioner_next_steps()` integration, tutorial notebook, and `llms-full.txt` HeterogeneousAdoptionDiD section (preserving UTF-8 fingerprint). README catalog + bundled `llms.txt` entry + `docs/api/had.rst` + `docs/references.rst` citation landed in PR #372 docs refresh. | `diff_diff/practitioner.py`, `tutorials/`, `diff_diff/guides/llms-full.txt` | Phase 2a | Low |
 | `HeterogeneousAdoptionDiD` time-varying dose on event study: Phase 2b REJECTS panels where `D_{g,t}` varies within a unit for `t >= F` (the aggregation uses `D_{g, F}` as the single regressor for all horizons, paper Appendix B.2 constant-dose convention). A follow-up PR could add a time-varying-dose estimator for these panels; current behavior is front-door rejection with a redirect to `ChaisemartinDHaultfoeuille`. | `diff_diff/had.py::_validate_had_panel_event_study` | Phase 2b | Low |
 | `HeterogeneousAdoptionDiD` repeated-cross-section support: paper Section 2 defines HAD on panel OR repeated cross-section, but Phase 2a is panel-only. RCS inputs (disjoint unit IDs between periods) are rejected by the balanced-panel validator with the generic "unit(s) do not appear in both periods" error. A follow-up PR will add an RCS identification path based on pre/post cell means (rather than unit-level first differences), with its own validator and a distinct `data_mode` / API surface. | `diff_diff/had.py::_validate_had_panel`, `diff_diff/had.py::_aggregate_first_difference` | Phase 2a | Medium |
 | SyntheticDiD: bootstrap cross-language parity anchor against R's default `synthdid::vcov(method="bootstrap")` (refit; rebinds `opts` per draw) or Julia `Synthdid.jl::src/vcov.jl::bootstrap_se` (refit by construction). Same-library validation (placebo-SE tracking, AER §6.3 MC truth) is in place; a cross-language anchor is desirable to bolster the methodology contract. Julia is the cleanest target — minimal wrapping work and refit-native vcov. Tolerance target: 1e-6 on Monte Carlo samples (different BLAS + RNG paths preclude 1e-10). The R-parity fixture from the previous release was deleted because it pinned the now-removed fixed-weight path. | `benchmarks/R/`, `benchmarks/julia/`, `tests/` | follow-up | Low |

diff_diff/guides/llms.txt

@@ -61,6 +61,7 @@ Full practitioner guide: call `diff_diff.get_llm_guide("practitioner")`
 - [SyntheticDiD](https://diff-diff.readthedocs.io/en/stable/api/estimators.html): Synthetic DiD combining standard DiD and synthetic control methods for few treated units
 - [TripleDifference](https://diff-diff.readthedocs.io/en/stable/api/triple_diff.html): Triple difference (DDD) estimator for designs requiring two criteria for treatment eligibility
 - [ContinuousDiD](https://diff-diff.readthedocs.io/en/stable/api/continuous_did.html): Callaway, Goodman-Bacon & Sant'Anna (2024) continuous treatment DiD with dose-response curves
+- [HeterogeneousAdoptionDiD](https://diff-diff.readthedocs.io/en/stable/api/had.html): de Chaisemartin, Ciccia, D'Haultfœuille & Knau (2026) for designs where **no unit remains untreated** (heterogeneous adoption / HAD); local-linear estimator targeting Weighted Average Slope (WAS) at the support boundary, with multi-period event-study extension. Alias `HAD`.
 - [StackedDiD](https://diff-diff.readthedocs.io/en/stable/api/stacked_did.html): Wing, Freedman & Hollingsworth (2024) stacked DiD with Q-weights and sub-experiments
 - [EfficientDiD](https://diff-diff.readthedocs.io/en/stable/api/efficient_did.html): Chen, Sant'Anna & Xie (2025) efficient DiD with optimal weighting for tighter SEs
 - [TROP](https://diff-diff.readthedocs.io/en/stable/api/trop.html): Triply Robust Panel estimator (Athey et al. 2025) with nuclear norm factor adjustment

docs/api/had.rst

@@ -0,0 +1,58 @@
+Heterogeneous Adoption Difference-in-Differences
+================================================
+
+Estimator for designs where **no unit remains untreated** at the post period
+- every unit `g` receives a strictly positive heterogeneous dose `D_{g,2} > 0`,
+and there is no genuinely untreated control group to anchor a standard DiD
+contrast.
+
+This module implements the methodology from de Chaisemartin, Ciccia,
+D'Haultfœuille & Knau (2026), "Difference-in-Differences Estimators When No
+Unit Remains Untreated" (arXiv:2405.04465v6), which:
+
+1. **Targets the Weighted Average Slope (WAS)** as the identified parameter
+   when no untreated comparison group exists (paper Equation 2).
+2. **Uses local-linear regression at the support boundary** to estimate the
+   slope of the dose-outcome relationship at the lowest observed dose.
+3. **Provides bias-corrected confidence intervals** (Calonico, Cattaneo & Titiunik
+   2014 / `nprobust`-style) and HC2 / Bell-McCaffrey small-sample SEs.
+4. **Extends to multi-period event-study settings** (paper Appendix B.2),
+   producing per-horizon WAS effects with correlated standard errors and
+   sup-t bands.
+
+.. note::
+
+   **When to use HAD.** Use ``HeterogeneousAdoptionDiD`` when your panel has
+   no untreated unit at any treatment period (e.g. universal-rollout policies,
+   industry-wide tariff changes) but treatment intensity varies across units.
+   For panels with a never-treated control group and continuous treatment,
+   use :class:`~diff_diff.ContinuousDiD` instead. For binary reversible
+   treatments, use :class:`~diff_diff.ChaisemartinDHaultfoeuille`.
+
+HeterogeneousAdoptionDiD
+------------------------
+
+.. autoclass:: diff_diff.HeterogeneousAdoptionDiD
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+HeterogeneousAdoptionDiDResults
+-------------------------------
+
+Single-period results container for ``HeterogeneousAdoptionDiD`` estimation.
+
+.. autoclass:: diff_diff.HeterogeneousAdoptionDiDResults
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+HeterogeneousAdoptionDiDEventStudyResults
+-----------------------------------------
+
+Multi-period event-study results container for the Appendix B.2 extension.
+
+.. autoclass:: diff_diff.HeterogeneousAdoptionDiDEventStudyResults
+   :members:
+   :undoc-members:
+   :show-inheritance:

docs/api/index.rst

@@ -24,6 +24,7 @@ Core estimator classes for DiD analysis:
    diff_diff.TripleDifference
    diff_diff.TROP
    diff_diff.ContinuousDiD
+   diff_diff.HeterogeneousAdoptionDiD
    diff_diff.EfficientDiD
    diff_diff.TwoStageDiD
    diff_diff.WooldridgeDiD
@@ -56,6 +57,8 @@ Result containers returned by estimators:
    diff_diff.TROPResults
    diff_diff.ContinuousDiDResults
    diff_diff.DoseResponseCurve
+   diff_diff.HeterogeneousAdoptionDiDResults
+   diff_diff.HeterogeneousAdoptionDiDEventStudyResults
    diff_diff.EfficientDiDResults
    diff_diff.EDiDBootstrapResults
    diff_diff.TwoStageDiDResults

docs/doc-deps.yaml

@@ -361,6 +361,37 @@ sources:
         section: "Varying Spending Levels"
         type: user_guide
 
+  # ── HeterogeneousAdoptionDiD (HAD) ──────────────────────────────────
+
+  diff_diff/had.py:
+    drift_risk: medium
+    docs:
+      - path: docs/methodology/REGISTRY.md
+        section: "HeterogeneousAdoptionDiD"
+        type: methodology
+      - path: docs/api/had.rst
+        type: api_reference
+      - path: README.md
+        section: "Estimators (one-line catalog entry)"
+        type: user_guide
+      - path: docs/references.rst
+        type: user_guide
+      - path: diff_diff/guides/llms.txt
+        section: "Estimators"
+        type: user_guide
+      # Note: llms-full.txt does not yet have a HeterogeneousAdoptionDiD section
+      # (deferred to TODO.md Phase 5 follow-up); the dependency mapping will be
+      # added when that section lands.
+
+  diff_diff/had_pretests.py:
+    drift_risk: medium
+    docs:
+      - path: docs/methodology/REGISTRY.md
+        section: "HeterogeneousAdoptionDiD"
+        type: methodology
+      - path: docs/api/had.rst
+        type: api_reference
+
   # ── SyntheticDiD ───────────��───────────────────────────────────────
 
   diff_diff/synthetic_did.py:

docs/references.rst

@@ -165,6 +165,13 @@ Continuous Treatment DiD
 
   Primary source for ATT(d), ACRT, dose-response curves, and B-spline flexibility implemented in our ``ContinuousDiD`` class.
 
+Heterogeneous Adoption (No-Untreated Designs)
+---------------------------------------------
+
+- **de Chaisemartin, C., Ciccia, D., D'Haultfœuille, X., & Knau, F. (2026).** "Difference-in-Differences Estimators When No Unit Remains Untreated." *arXiv preprint* arXiv:2405.04465v6. https://arxiv.org/abs/2405.04465
+
+  Primary source for the Weighted Average Slope (WAS) estimator and its multi-period event-study extension implemented in our ``HeterogeneousAdoptionDiD`` class. Targets settings where every unit receives a strictly positive heterogeneous dose at treatment time, using local-linear regression at the support boundary.
+
 Power Analysis
 --------------