Address PR #409 R1 review (1 P1, 1 P2)

P1 — Decouple QUG from HAD's `design="auto"` selector across all surfaces.
The two are independent rules: QUG is a statistical pre-test on
`H0: d_lower = 0`; `_detect_design()` is a min/median heuristic
(`continuous_at_zero` fires when `d.min() < 0.01 * median(|d|)`). On
T21's panel both checks point to the same identification path but the
mechanisms are independent — `_detect_design()` does not consume the
QUG p-value. Reword tutorial Section 2 + Section 3, CHANGELOG entry,
and drift-test docstrings to reflect this.

Add `test_had_design_auto_lands_on_continuous_at_zero`: explicitly
fits `HAD(design="auto")` on the two-period panel and asserts
`design == "continuous_at_zero"` and `target_parameter == "WAS"`,
locking the prose claim independently of the QUG-test pins.

P2 — Update REGISTRY.md to mark T21 shipped (PR #409); leave T22 row
queued.

All 16 drift tests pass on both backends; notebook executes cleanly.

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

Author: igerber

CHANGELOG.md

@@ -8,7 +8,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 ## [Unreleased]
 
 ### Added
-- **Tutorial 21: HAD Pre-test Workflow** (`docs/tutorials/21_had_pretest_workflow.ipynb`) — composite pre-test walkthrough for `HeterogeneousAdoptionDiD` building on Tutorial 20's brand-campaign framing. Uses a 60-DMA × 8-week panel close in shape to T20's but with the dose distribution drawn from `Uniform[$0.01K, $50K]` (vs T20's `[$5K, $50K]`); the true support is strictly positive but very near zero, chosen so the QUG step in `did_had_pretest_workflow` fails-to-reject `H0: d_lower = 0` in this finite sample and the verdict text fires the load-bearing "Assumption 7 deferred" pivot for the upgrade-arc narrative. (HAD's `design="auto"` rule then selects the `continuous_at_zero` identification path with target `WAS` based on the QUG outcome — a workflow decision following the test result, not a property of the true DGP support.) Walks through three surfaces: (a) `did_had_pretest_workflow(aggregate="overall")` on a two-period collapse, where the verdict explicitly flags Step 2 (Assumption 7 pre-trends) as not run because a single pre-period structurally cannot support a pre-trends test, and the structural fields `pretrends_joint` / `homogeneity_joint` are both `None`; (b) `did_had_pretest_workflow(aggregate="event_study")` on the full multi-period panel, where the verdict reads "TWFE admissible under Section 4 assumptions" because all three testable diagnostics (QUG + joint pre-trends Stute over 3 horizons + joint homogeneity Stute over 4 horizons) fail-to-reject — non-rejection evidence under finite-sample power and test specification, not proof that the identifying assumptions hold; and (c) a side panel exercising both `yatchew_hr_test` null modes — `null="linearity"` (default, paper Theorem 7) vs `null="mean_independence"` (Phase 4 R-parity with R `YatchewTest::yatchew_test(order=0)`) — on the within-pre-period first-difference paired with post-period dose, illustrating the stricter null's larger residual variance (`sigma2_lin` 7.01 vs 6.53) and smaller p-value (0.29 vs 0.49). Companion drift-test file `tests/test_t21_had_pretest_workflow_drift.py` (15 tests pinning panel composition, both verdict pivots, structural anchors on both paths, deterministic QUG / Yatchew statistics, and bootstrap p-value tolerance bands per `feedback_bootstrap_drift_tests_need_backend_tolerance`). T20's "Composite pretest workflow" Extensions bullet updated with a forward-pointer to T21. T22 weighted/survey HAD tutorial remains queued as a separate notebook PR.
+- **Tutorial 21: HAD Pre-test Workflow** (`docs/tutorials/21_had_pretest_workflow.ipynb`) — composite pre-test walkthrough for `HeterogeneousAdoptionDiD` building on Tutorial 20's brand-campaign framing. Uses a 60-DMA × 8-week panel close in shape to T20's but with the dose distribution drawn from `Uniform[$0.01K, $50K]` (vs T20's `[$5K, $50K]`); the true support is strictly positive but very near zero, chosen so the QUG step in `did_had_pretest_workflow` fails-to-reject `H0: d_lower = 0` in this finite sample and the verdict text fires the load-bearing "Assumption 7 deferred" pivot for the upgrade-arc narrative. (HAD's `design="auto"` selector — a separate min/median heuristic at `had.py::_detect_design`, NOT the QUG p-value — independently lands on the `continuous_at_zero` identification path with target `WAS` on this panel because `d.min() < 0.01 * median(|d|)`. The QUG test and the design selector are independent rules that point to the same identification path here.) Walks through three surfaces: (a) `did_had_pretest_workflow(aggregate="overall")` on a two-period collapse, where the verdict explicitly flags Step 2 (Assumption 7 pre-trends) as not run because a single pre-period structurally cannot support a pre-trends test, and the structural fields `pretrends_joint` / `homogeneity_joint` are both `None`; (b) `did_had_pretest_workflow(aggregate="event_study")` on the full multi-period panel, where the verdict reads "TWFE admissible under Section 4 assumptions" because all three testable diagnostics (QUG + joint pre-trends Stute over 3 horizons + joint homogeneity Stute over 4 horizons) fail-to-reject — non-rejection evidence under finite-sample power and test specification, not proof that the identifying assumptions hold; and (c) a side panel exercising both `yatchew_hr_test` null modes — `null="linearity"` (default, paper Theorem 7) vs `null="mean_independence"` (Phase 4 R-parity with R `YatchewTest::yatchew_test(order=0)`) — on the within-pre-period first-difference paired with post-period dose, illustrating the stricter null's larger residual variance (`sigma2_lin` 7.01 vs 6.53) and smaller p-value (0.29 vs 0.49). Companion drift-test file `tests/test_t21_had_pretest_workflow_drift.py` (15 tests pinning panel composition, both verdict pivots, structural anchors on both paths, deterministic QUG / Yatchew statistics, and bootstrap p-value tolerance bands per `feedback_bootstrap_drift_tests_need_backend_tolerance`). T20's "Composite pretest workflow" Extensions bullet updated with a forward-pointer to T21. T22 weighted/survey HAD tutorial remains queued as a separate notebook PR.
 - **`ChaisemartinDHaultfoeuille.by_path` and `paths_of_interest` now compose with `survey_design`** for analytical Binder TSL SE and replicate-weight bootstrap variance. The `NotImplementedError` gate at `chaisemartin_dhaultfoeuille.py:1233-1239` is replaced by a per-path multiplier-bootstrap-only gate (`survey_design + n_bootstrap > 0` under by_path / paths_of_interest still raises, since the survey-aware perturbation pivot for path-restricted IFs is methodologically underived). Per-path SE routes through the existing `_survey_se_from_group_if` cell-period allocator: the per-period IF (`U_pp_l_path`) is built with non-path switcher-side contributions skipped (control contributions are unchanged, matching the joiners/leavers IF convention; preserves the row-sum identity `U_pp.sum(axis=1) == U`), cohort-recentered via `_cohort_recenter_per_period`, then expanded to observations as `psi_i = U_pp[g_i, t_i] · (w_i / W_{g_i, t_i})`. Replicate-weight designs unconditionally use the cell allocator (Class A contract from PR #323). New `_refresh_path_inference` helper post-call refreshes `safe_inference` on every populated entry across `multi_horizon_inference`, `placebo_horizon_inference`, `path_effects`, and `path_placebos` so all four surfaces use the same final `df_survey` after per-path replicate fits append `n_valid` to the shared accumulator. Path-enumeration ranking under `survey_design` remains unweighted (group-cardinality, not population-weight mass). Lonely-PSU policy stays sample-wide, not per-path. Telescope invariant: on a single-path panel, per-path SE matches the global non-by_path survey SE bit-exactly. **No R parity** — R `did_multiplegt_dyn` does not support survey weighting; this is a Python-only methodology extension. The global non-by_path TSL multiplier-bootstrap path is unaffected (anti-regression test `tests/test_chaisemartin_dhaultfoeuille.py::TestByPathSurveyDesignAnalytical::test_global_survey_plus_n_bootstrap_still_works` locks the per-path-only scope of the new gate). Cross-surface invariants regression-tested at `TestByPathSurveyDesignAnalytical` (~17 tests across gate / dispatch / analytical SE / replicate-weight SE / per-path placebos / `trends_linear` composition / unobserved-path warnings / final-df refresh regressions) and `TestByPathSurveyDesignTelescope`. See `docs/methodology/REGISTRY.md` §`ChaisemartinDHaultfoeuille` `Note (Phase 3 by_path ...)` → "Per-path survey-design SE" for the full contract.
 - **Inference-field aliases on staggered result classes** for adapter / external-consumer compatibility. Read-only `@property` aliases expose the flat `att` / `se` / `conf_int` / `p_value` / `t_stat` names (matching `DiDResults` / `TROPResults` / `SyntheticDiDResults` / `HeterogeneousAdoptionDiDResults`) on every result class that previously only carried prefixed canonical fields: `CallawaySantAnnaResults`, `StackedDiDResults`, `EfficientDiDResults`, `ChaisemartinDHaultfoeuilleResults`, `StaggeredTripleDiffResults`, `WooldridgeDiDResults`, `SunAbrahamResults`, `ImputationDiDResults`, `TwoStageDiDResults` (mapping to `overall_*`); `ContinuousDiDResults` (mapping to `overall_att_*`, ATT-side as the headline, ACRT-side accessible unchanged via `overall_acrt_*`); `MultiPeriodDiDResults` (mapping to `avg_*`). `ContinuousDiDResults` additionally exposes `overall_se` / `overall_conf_int` / `overall_p_value` / `overall_t_stat` aliases for naming consistency with the rest of the staggered family. Aliases are pure read-throughs over the canonical fields — no recomputation, no behavior change — so the `safe_inference()` joint-NaN contract (per CLAUDE.md "Inference computation") is inherited automatically (NaN canonical → NaN alias, locked at `tests/test_result_aliases.py::test_pattern_b_aliases_propagate_nan`). The native `overall_*` / `overall_att_*` / `avg_*` fields remain canonical for documentation and computation. Motivated by the `balance.interop.diff_diff.as_balance_diagnostic()` adapter (`facebookresearch/balance` PR #465) which calls `getattr(res, "se", None)` / `getattr(res, "conf_int", None)` without a fallback chain — pre-alias, every staggered result class returned `None` on those keys, silently dropping `se` and `conf_int` from the adapter's diagnostic dict. 23 alias-mechanic + balance-adapter regression tests at `tests/test_result_aliases.py`. Patch-level (additive on stable surfaces).
 - **`ChaisemartinDHaultfoeuille.by_path` + non-binary integer treatment** — `by_path=k` now accepts integer-coded discrete treatment (D in Z, e.g. ordinal `{0, 1, 2}`); path tuples become integer-state tuples like `(0, 2, 2, 2)`. The previous `NotImplementedError` gate at `chaisemartin_dhaultfoeuille.py:1870` is replaced by a `ValueError` for continuous D (e.g. `D=1.5`) at fit-time per the no-silent-failures contract — the existing `int(round(float(v)))` cast in `_enumerate_treatment_paths` is now defensive (no-op for integer-coded D). Validated against R `did_multiplegt_dyn(..., by_path)` for D in `{0, 1, 2}` via the new `multi_path_reversible_by_path_non_binary` golden-value scenario (78 switchers, 3 paths, single-baseline custom DGP, F_g >= 4): per-path point estimates match R bit-exactly (rtol ~1e-9 on event horizons; rtol+atol envelope for placebo near-zero values), per-path SE inherits the documented cross-path cohort-sharing deviation (~5% rtol observed; SE_RTOL=0.15 envelope). **Deviation from R for D >= 10:** R's `did_multiplegt_by_path` derives the per-path baseline via `path_index$baseline_XX <- substr(path_index$path, 1, 1)`, which captures only the first character of the comma-separated path string (e.g. for `path = "12,12,..."` it captures `"1"` instead of `"12"`); this mis-allocates R's per-path control-pool subset for D >= 10. Python's tuple-key matching is correct in this regime — the per-path point estimates we compute are correct; R's per-path subset for the same path is buggy. The shipped parity scenario stays in `D in {0, 1, 2}` to avoid the R bug. R-parity test at `tests/test_chaisemartin_dhaultfoeuille_parity.py::TestDCDHDynRParityByPathNonBinary`; cross-surface invariants regression-tested at `tests/test_chaisemartin_dhaultfoeuille.py::TestByPathNonBinary`.

docs/methodology/REGISTRY.md

@@ -2553,7 +2553,8 @@ Shipped in `diff_diff/had_pretests.py` as `stute_joint_pretest()` (residuals-in
 - [x] Phase 5 (wave 1, PR #402): `practitioner_next_steps()` integration for HAD results - `_handle_had` and `_handle_had_event_study` route both result classes through HAD-specific Baker et al. (2025) step guidance with bidirectional HAD ↔ ContinuousDiD Step-4 routing closure. The `_check_nan_att` helper extends to ndarray `att` (HAD event-study) via `np.all(np.isnan(arr))` semantics; scalar path bit-exact preserved.
 - [x] Phase 5 (wave 1, PR #402): `llms-full.txt` HeterogeneousAdoptionDiD section + result-class blocks + `## HAD Pretests` index + Choosing-an-Estimator row landed; constructor / fit() signatures match the real API (regression-tested via `inspect.signature`); result-class field tables enumerate every public dataclass field (regression-tested via `dataclasses.fields()`); `llms-practitioner.txt` Step 4 decision tree distinguishes ContinuousDiD (per-dose ATT(d), needs never-treated) from HeterogeneousAdoptionDiD (WAS, universal-rollout-compatible).
 - [x] Phase 5 (partial): README catalog one-liner, bundled `llms.txt` `## Estimators` entry, `docs/api/had.rst` (autoclass for the three classes), and `docs/references.rst` citation landed in PR #372 docs refresh.
-- [ ] Phase 5 (remaining): T21 HAD pretest workflow tutorial + T22 weighted/survey HAD tutorial - tracked in `TODO.md`.
+- [x] Phase 5 (wave 2 first slice, PR #409): T21 HAD pretest workflow tutorial (`docs/tutorials/21_had_pretest_workflow.ipynb`) — composite pre-test walkthrough for `did_had_pretest_workflow`. Uses a `Uniform[$0.01K, $50K]` dose-distribution variant of T20's brand-campaign panel (true support strictly positive but near-zero, chosen so QUG fails-to-reject `H0: d_lower = 0` in finite sample). Walks through `aggregate="overall"` (Steps 1 + 3 only, verdict explicitly flags Step 2 deferral) and upgrades to `aggregate="event_study"` (joint pre-trends Stute + joint homogeneity Stute close the gap). Side panel exercises both `yatchew_hr_test` null modes (`linearity` vs `mean_independence`). Companion drift-test file `tests/test_t21_had_pretest_workflow_drift.py` (16 tests pinning panel composition, both verdict pivots, structural anchors, deterministic stats, bootstrap p-value tolerance bands per backend, and `HAD(design="auto")` resolution to `continuous_at_zero` on this panel).
+- [ ] Phase 5 (remaining): T22 weighted/survey HAD tutorial - tracked in `TODO.md`.
 - [ ] Documentation of non-testability of Assumptions 5 and 6.
 - [ ] Warnings for staggered treatment timing (redirect to `ChaisemartinDHaultfoeuille`).
 - [ ] `NotImplementedError` phase pointer when `covariates=` is passed (Theorem 6 future work).