Address PR #372 R9 review (1 P3): HAD WAS vs WAS_{d̲} estimand distinction

P3 - HAD summary copy compressed both Design 1' and Design 1 into "targets
WAS", but the shipped class contract distinguishes them:
- Design 1' (`d̲ = 0`, `continuous_at_zero` path): identifies the original
  Weighted Average Slope (WAS, paper Equation 2). `target_parameter == "WAS"`
  on the result class.
- Design 1 (`d̲ > 0`, `continuous_near_d_lower` or `mass_point` paths):
  identifies `WAS_{d̲}` under Assumption 6 (or sign identification only
  under Assumption 5; both are non-testable via pre-trends).
  `target_parameter == "WAS_d_lower"` on the result class.

Rewrite the HAD summaries in three places (docs/api/had.rst, README.md
catalog one-liner, diff_diff/guides/llms.txt one-liner) to surface the
distinction explicitly. The api/had.rst summary also flags that callers
can key on the resolved `target_parameter` attribute, and that Design 1
identification requires the additional non-testable assumption beyond
parallel trends.

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

Author: igerber

README.md

@@ -110,7 +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 designs where **no unit remains untreated**; local-linear estimator targeting the Weighted Average Slope (WAS) at the dose support boundary (Design 1' with `d̲ = 0` and Design 1 with `d̲ > 0`), with a multi-period event-study extension (last-treatment cohort, pointwise CIs). Alias `HAD`.
+- [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**; local-linear estimator at the dose support boundary returning Weighted Average Slope (WAS) on Design 1' (`d̲ = 0` / QUG) or `WAS_{d̲}` on Design 1 (`d̲ > 0`, continuous-near-d̲ or mass-point), with a multi-period event-study extension (last-treatment cohort, pointwise CIs). 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

diff_diff/guides/llms.txt

@@ -61,7 +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**; local-linear estimator targeting Weighted Average Slope (WAS) at the dose support boundary (Design 1' with `d̲=0` and Design 1 with `d̲>0`), with multi-period event-study extension (last-treatment cohort, pointwise CIs). Alias `HAD`.
+- [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**; local-linear estimator at the dose support boundary returning Weighted Average Slope (WAS) on Design 1' (`d̲=0` / QUG) or `WAS_{d̲}` on Design 1 (`d̲>0`, continuous-near-d̲ or mass-point), with multi-period event-study extension (last-treatment cohort, pointwise CIs). 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

@@ -10,11 +10,18 @@ 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. **Estimates WAS via local-linear regression at the dose support boundary**
-   for both Design 1' (the QUG / Quasi-Untreated-Group case where the support
-   infimum ``d̲ = 0``) and Design 1 (no QUG, ``d̲ > 0``).
+1. **Targets WAS or WAS_{d̲} depending on design path:** Design 1' (the
+   QUG / Quasi-Untreated-Group case with ``d̲ = 0``) identifies the
+   Weighted Average Slope (WAS, paper Equation 2); Design 1 (no QUG,
+   ``d̲ > 0``) identifies ``WAS_{d̲}`` under Assumption 6, or sign
+   identification only under Assumption 5 (neither additional assumption
+   is testable via pre-trends). The shipped result classes expose
+   ``target_parameter == "WAS"`` versus ``"WAS_d_lower"`` so callers can
+   key on the resolved estimand.
+2. **Estimates the target via local-linear regression at the dose support
+   boundary**, with three concrete fit paths: ``continuous_at_zero`` for
+   Design 1', and ``continuous_near_d_lower`` or ``mass_point`` for
+   Design 1 (auto-detected from the dose distribution).
 3. **Provides bias-corrected confidence intervals** ported from the
    ``nprobust`` machinery for the continuous-dose paths, and a
    structural-residual 2SLS sandwich for the mass-point path.