Address PR #366 CI review round 7 (1 P3): include non-negativity clause in CHANGELOG is_count_like description

Reviewer correctly noted that the CHANGELOG bullet describing
`is_count_like` listed only four of the five conditions
(integer-valued + has zeros + right-skewed + > 2 distinct values)
but omitted the `value_min >= 0` non-negativity clause added in
round 4. Readers of the release notes alone would have expected
negative-valued count-like outcomes to route as `is_count_like=True`
even though the implementation intentionally suppresses that to stay
compatible with `WooldridgeDiD(method="poisson")`'s hard non-negative
requirement (`wooldridge.py:1105-1109`).

Updated the bullet to include the non-negativity clause and
explicitly cite the wooldridge.py line range so the release-notes
description matches `diff_diff/profile.py`,
`diff_diff/guides/llms-autonomous.txt`, and the
`test_outcome_shape_count_like_excludes_negative_support`
regression.

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

Author: igerber

CHANGELOG.md

@@ -9,7 +9,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ### Added
 - **`HeterogeneousAdoptionDiD` mass-point `survey=` / `weights=` + event-study `aggregate="event_study"` survey composition + multiplier-bootstrap sup-t simultaneous confidence band (Phase 4.5 B).** Closes the two Phase 4.5 A `NotImplementedError` gates: `design="mass_point" + weights/survey` and `aggregate="event_study" + weights/survey`. Weighted 2SLS sandwich in `_fit_mass_point_2sls` follows the Wooldridge 2010 Ch. 12 pweight convention (`w²` in the HC1 meat, `w·u` in the CR1 cluster score, weighted bread `Z'WX`); HC1 and CR1 ("stata" `se_type`) bit-parity with `estimatr::iv_robust(..., weights=, clusters=)` at `atol=1e-10` (new cross-language golden at `benchmarks/data/estimatr_iv_robust_golden.json`, generated by `benchmarks/R/generate_estimatr_iv_robust_golden.R`; `estimatr` added to `benchmarks/R/requirements.R`). `_fit_mass_point_2sls` gains `weights=` + `return_influence=` kwargs and now always returns a 3-tuple `(beta, se, psi)` — `psi` is the per-unit IF on the β̂-scale scaled so `compute_survey_if_variance(psi, trivial_resolved) ≈ V_HC1[1,1]` at `atol=1e-10` (PR #359 IF scale convention applied uniformly; no `sum(psi²)` claims). Event-study per-horizon variance: `survey=` path composes Binder-TSL via `compute_survey_if_variance`; `weights=` shortcut uses the analytical weighted-robust SE (continuous: CCT-2014 `bc_fit.se_robust / |den|`; mass-point: weighted 2SLS pweight sandwich from `_fit_mass_point_2sls` — HC1 / classical / CR1). `survey_metadata` / `variance_formula` / `effective_dose_mean` populated in both regimes (previously hardcoded `None` at `had.py:3366`). New multiplier-bootstrap sup-t: `_sup_t_multiplier_bootstrap` reuses `diff_diff.bootstrap_utils.generate_survey_multiplier_weights_batch` for PSU-level draws with stratum centering + sqrt(n_h/(n_h-1)) small-sample correction + FPC scaling + lonely-PSU handling. On the `weights=` shortcut, sup-t calibration is routed through a synthetic trivial `ResolvedSurveyDesign` so the centered + small-sample-corrected branch fires uniformly — targets the analytical HC1 variance family (`compute_survey_if_variance(IF, trivial) ≈ V_HC1` per the PR #359 IF scale invariant) rather than the raw `sum(ψ²) = ((n-1)/n) · V_HC1` that unit-level Rademacher multipliers would produce on the HC1-scaled IF. Perturbations: `delta = weights @ IF` with NO `(1/n)` prefactor (matching `staggered_bootstrap.py:373` idiom), normalized by per-horizon analytical SE, `(1-alpha)`-quantile of the sup-t distribution. At H=1 the quantile reduces to `Φ⁻¹(1 − alpha/2) ≈ 1.96` up to MC noise (regression-locked by `TestSupTReducesToNormalAtH1`). `HeterogeneousAdoptionDiD.__init__` gains `n_bootstrap: int = 999` and `seed: Optional[int] = None` (CS-parity singular seed); `fit()` gains `cband: bool = True` (only consulted on weighted event-study). `HeterogeneousAdoptionDiDEventStudyResults` extended with `variance_formula`, `effective_dose_mean`, `cband_low`, `cband_high`, `cband_crit_value`, `cband_method`, `cband_n_bootstrap` (all `None` on unweighted fits); surfaced in `to_dict`, `to_dataframe`, `summary`, `__repr__`. Unweighted event-study with `cband=False` preserves pre-Phase 4.5 B numerical output bit-exactly (stability invariant, locked by regression tests). Zero-weight subpopulation convention carries over from PR #359 (filter for design decisions; preserve full ResolvedSurveyDesign for variance). Non-pweight SurveyDesigns (`aweight`, `fweight`, replicate designs) raise `NotImplementedError` on both new paths (reciprocal-guard discipline). Pretest surfaces (`qug_test`, `stute_test`, `yatchew_hr_test`, joint variants, `did_had_pretest_workflow`) remain unweighted in this release — Phase 4.5 C / C0. See `docs/methodology/REGISTRY.md` §HeterogeneousAdoptionDiD "Weighted 2SLS (Phase 4.5 B)", "Event-study survey composition", and "Sup-t multiplier bootstrap" for derivations and invariants.
-- **`PanelProfile.outcome_shape` and `PanelProfile.treatment_dose` extensions + `llms-autonomous.txt` worked examples (Wave 2 of the AI-agent enablement track).** `profile_panel(...)` now populates two new optional sub-dataclasses on the returned `PanelProfile`: `outcome_shape: Optional[OutcomeShape]` (numeric outcomes only — exposes `n_distinct_values`, `pct_zeros`, `value_min` / `value_max`, `skewness` and `excess_kurtosis` (NaN-safe; `None` when `n_distinct_values < 3` or variance is zero), `is_integer_valued`, `is_count_like` (heuristic: integer-valued AND has zeros AND right-skewed AND > 2 distinct values; flags WooldridgeDiD QMLE consideration over linear OLS), `is_bounded_unit` ([0, 1] support)) and `treatment_dose: Optional[TreatmentDoseShape]` (continuous treatments only — exposes `n_distinct_doses`, `has_zero_dose`, `dose_min` / `dose_max` / `dose_mean` over non-zero doses). Both `OutcomeShape` and `TreatmentDoseShape` are mostly descriptive context, with `treatment_dose.dose_min > 0` doing double duty as a profile-side screening check for `ContinuousDiD` (the estimator rejects negative treated doses at line 287-294 of `continuous_did.py`). The profile-side screening set for `ContinuousDiD` is `PanelProfile.has_never_treated` (unit-level), `PanelProfile.treatment_varies_within_unit == False` (per-unit full-path dose constancy, matching `ContinuousDiD.fit()`'s `df.groupby(unit)[dose].nunique() > 1` rejection), `PanelProfile.is_balanced`, absence of the `duplicate_unit_time_rows` alert (the precompute path silently resolves duplicate `(unit, time)` cells via last-row-wins), and `treatment_dose.dose_min > 0`. These checks are necessary but not sufficient: `ContinuousDiD.fit()` takes a separate `first_treat` column (not seen by `profile_panel`) and applies additional validation (NaN/inf/negative rejection, dose=0 unit drops on `first_treat > 0`, force-zero on `first_treat == 0` with nonzero dose). Treat the profile-side set as a pre-flight screen, not the complete contract. The shape extensions provide distributional context (effect-size range, count-shape detection) that supplements but does not replace those gates. Both fields are `None` when their classification gate is not met (e.g., `treatment_dose is None` for binary treatments). `to_dict()` serializes the nested dataclasses as JSON-compatible nested dicts. New exports: `OutcomeShape`, `TreatmentDoseShape` from top-level `diff_diff`. `llms-autonomous.txt` gains a new §5 "Worked examples" section with three end-to-end PanelProfile -> reasoning -> validation walkthroughs (binary staggered with never-treated controls, continuous dose with zero baseline, count-shaped outcome) plus §2 field-reference subsections for the new shape fields and §4.7 / §4.11 cross-references for outcome-shape considerations. Existing §5-§8 of the autonomous guide are renumbered to §6-§9. Descriptive only — no recommender language inside the worked examples.
+- **`PanelProfile.outcome_shape` and `PanelProfile.treatment_dose` extensions + `llms-autonomous.txt` worked examples (Wave 2 of the AI-agent enablement track).** `profile_panel(...)` now populates two new optional sub-dataclasses on the returned `PanelProfile`: `outcome_shape: Optional[OutcomeShape]` (numeric outcomes only — exposes `n_distinct_values`, `pct_zeros`, `value_min` / `value_max`, `skewness` and `excess_kurtosis` (NaN-safe; `None` when `n_distinct_values < 3` or variance is zero), `is_integer_valued`, `is_count_like` (heuristic: integer-valued AND has zeros AND right-skewed AND > 2 distinct values AND non-negative support, i.e. `value_min >= 0`; flags WooldridgeDiD QMLE consideration over linear OLS — the non-negativity clause aligns the routing signal with `WooldridgeDiD(method="poisson")`'s hard rejection of negative outcomes at `wooldridge.py:1105-1109`), `is_bounded_unit` ([0, 1] support)) and `treatment_dose: Optional[TreatmentDoseShape]` (continuous treatments only — exposes `n_distinct_doses`, `has_zero_dose`, `dose_min` / `dose_max` / `dose_mean` over non-zero doses). Both `OutcomeShape` and `TreatmentDoseShape` are mostly descriptive context, with `treatment_dose.dose_min > 0` doing double duty as a profile-side screening check for `ContinuousDiD` (the estimator rejects negative treated doses at line 287-294 of `continuous_did.py`). The profile-side screening set for `ContinuousDiD` is `PanelProfile.has_never_treated` (unit-level), `PanelProfile.treatment_varies_within_unit == False` (per-unit full-path dose constancy, matching `ContinuousDiD.fit()`'s `df.groupby(unit)[dose].nunique() > 1` rejection), `PanelProfile.is_balanced`, absence of the `duplicate_unit_time_rows` alert (the precompute path silently resolves duplicate `(unit, time)` cells via last-row-wins), and `treatment_dose.dose_min > 0`. These checks are necessary but not sufficient: `ContinuousDiD.fit()` takes a separate `first_treat` column (not seen by `profile_panel`) and applies additional validation (NaN/inf/negative rejection, dose=0 unit drops on `first_treat > 0`, force-zero on `first_treat == 0` with nonzero dose). Treat the profile-side set as a pre-flight screen, not the complete contract. The shape extensions provide distributional context (effect-size range, count-shape detection) that supplements but does not replace those gates. Both fields are `None` when their classification gate is not met (e.g., `treatment_dose is None` for binary treatments). `to_dict()` serializes the nested dataclasses as JSON-compatible nested dicts. New exports: `OutcomeShape`, `TreatmentDoseShape` from top-level `diff_diff`. `llms-autonomous.txt` gains a new §5 "Worked examples" section with three end-to-end PanelProfile -> reasoning -> validation walkthroughs (binary staggered with never-treated controls, continuous dose with zero baseline, count-shaped outcome) plus §2 field-reference subsections for the new shape fields and §4.7 / §4.11 cross-references for outcome-shape considerations. Existing §5-§8 of the autonomous guide are renumbered to §6-§9. Descriptive only — no recommender language inside the worked examples.
 - **`HeterogeneousAdoptionDiD.fit(survey=..., weights=...)` on continuous-dose paths (Phase 4.5 survey support).** The `continuous_at_zero` (paper Design 1') and `continuous_near_d_lower` (Design 1 continuous-near-d̲) designs accept survey weights through two interchangeable kwargs: `weights=<array>` (pweight shortcut, weighted-robust SE from the CCT-2014 lprobust port) and `survey=SurveyDesign(weights, strata, psu, fpc)` (design-based inference via Binder-TSL variance using the existing `compute_survey_if_variance` helper at `diff_diff/survey.py:1802`). Point estimates match across both entry paths; SE diverges by design (pweight-only vs PSU-aggregated). `HeterogeneousAdoptionDiDResults.survey_metadata` is a repo-standard `SurveyMetadata` dataclass (weight_type / effective_n / design_effect / sum_weights / weight_range / n_strata / n_psu / df_survey); HAD-specific extras (`variance_formula` label, `effective_dose_mean`) are separate top-level result fields. `to_dict()` surfaces the full `SurveyMetadata` object plus `variance_formula` + `effective_dose_mean`; `summary()` renders `variance_formula`, `effective_n`, `effective_dose_mean`, and (when the survey= path is used) `df_survey`; `__repr__` surfaces `variance_formula` + `effective_dose_mean` when present. The HAD `mass_point` design and `aggregate="event_study"` path raise `NotImplementedError` under survey/weights (deferred to Phase 4.5 B: weighted 2SLS + event-study survey composition); the HAD pretests stay unweighted in this release (Phase 4.5 C). Parity ceiling acknowledged — no public weighted-CCF bias-corrected local-linear reference exists in any language; methodology confidence comes from (1) uniform-weights bit-parity at `atol=1e-14` on the full lprobust output struct, (2) cross-language weighted-OLS parity (manual R reference) at `atol=1e-12`, and (3) Monte Carlo oracle consistency on known-τ DGPs. `_nprobust_port.lprobust` gains `weights=` and `return_influence=` (used internally by the Binder-TSL path); `bias_corrected_local_linear` removes the Phase 1c `NotImplementedError` on `weights=` and forwards. Auto-bandwidth selection remains unweighted in this release — pass `h`/`b` explicitly for weight-aware bandwidths. See `docs/methodology/REGISTRY.md` §HeterogeneousAdoptionDiD "Weighted extension (Phase 4.5 survey support)".
 - **`stute_joint_pretest`, `joint_pretrends_test`, `joint_homogeneity_test` + `StuteJointResult`** (HeterogeneousAdoptionDiD Phase 3 follow-up). Joint Cramér-von Mises pretests across K horizons with shared-η Mammen wild bootstrap (preserves vector-valued empirical-process unit-level dependence per Delgado-Manteiga 2001 / Hlávka-Hušková 2020). The core `stute_joint_pretest` is residuals-in; two thin data-in wrappers construct per-horizon residuals for the two nulls the paper spells out: mean-independence (step 2 pre-trends, `OLS(Y_t − Y_base ~ 1)` per pre-period) and linearity (step 3 joint, `OLS(Y_t − Y_base ~ 1 + D)` per post-period). Sum-of-CvMs aggregation (`S_joint = Σ_k S_k`); per-horizon scale-invariant exact-linear short-circuit. Closes the paper Section 4.2 step-2 gap that Phase 3 `did_had_pretest_workflow` previously flagged with an "Assumption 7 pre-trends test NOT run" caveat. See `docs/methodology/REGISTRY.md` §HeterogeneousAdoptionDiD "Joint Stute tests" for algorithm, invariants, and scope exclusion of Eq 18 linear-trend detrending (deferred to Phase 4 Pierce-Schott replication).
 - **`did_had_pretest_workflow(aggregate="event_study")`**: multi-period dispatch on balanced ≥3-period panels. Runs QUG at `F` + joint pre-trends Stute across earlier pre-periods + joint homogeneity-linearity Stute across post-periods. Step 2 closure requires ≥2 pre-periods; with only a single pre-period (the base `F-1`) `pretrends_joint=None` and the verdict flags the skip. Reuses the Phase 2b event-study panel validator (last-cohort auto-filter under staggered timing with `UserWarning`; `ValueError` when `first_treat_col=None` and the panel is staggered). The data-in wrappers `joint_pretrends_test` and `joint_homogeneity_test` also route through that same validator internally, so direct wrapper calls inherit the last-cohort filter and constant-post-dose invariant. `HADPretestReport` extended with `pretrends_joint`, `homogeneity_joint`, and `aggregate` fields; serialization methods (`summary`, `to_dict`, `to_dataframe`, `__repr__`) preserve the Phase 3 output bit-exactly on `aggregate="overall"` — no `aggregate` key, no header row, no schema drift — and only surface the new fields on `aggregate="event_study"`.