Address PR #365 R7 P3: gate _loo_unit_ids on unit-granularity; refresh harness docstring

P3 (Maintainability — survey jackknife still populated _loo_unit_ids):
``fit()`` was unconditionally setting ``_loo_unit_ids`` / ``_loo_roles``
on every jackknife fit, including full-design survey fits where the
underlying replicates are PSU-level and ``get_loo_effects_df()`` now
raises ``NotImplementedError``. Internal / canned guidance keyed off
``_loo_unit_ids is not None`` as the availability check (e.g.,
``practitioner.py``) would still call the accessor on a survey fit and
hit the new raise.

Fix: only populate ``_loo_unit_ids`` / ``_loo_roles`` when
``_loo_granularity == "unit"``; leave them ``None`` on the PSU path so
``_loo_unit_ids is not None`` correctly reports availability.
``_loo_granularity`` is the authoritative accessor gate; the legacy
``_loo_unit_ids`` sentinel now agrees with it.

P3 (Documentation — harness docstring stale):
``coverage_sdid.py::_fit_one`` docstring said "fit() routes [survey
designs] through the bootstrap survey path (PR #352) when
method=='bootstrap'" — stale after the placebo + jackknife full-
design paths landed. Rewrote to describe the three method-specific
survey variance paths (weighted-FW + Rao-Wu bootstrap; stratified-
permutation + weighted-FW placebo; PSU-LOO + stratum-aggregation
jackknife) and mention the Case B-D ValueError failure modes alongside
NotImplementedError.

Verification: 94 passed (no behavior change on the gating fix — it's
a state-gating tightening, not a correctness change).

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

Author: igerber

benchmarks/python/coverage_sdid.py

@@ -297,10 +297,14 @@ def _fit_one(
     """Fit SDID and return (att, se, p_value); (None, None, None) on failure.
 
     For survey DGPs the harness passes a SurveyDesign via ``survey_design``;
-    fit() routes it through the bootstrap survey path (PR #352) when
-    method=='bootstrap'. The DGP's ``survey_design_factory`` declares which
-    methods are supported, so the caller skips unsupported methods entirely
-    rather than catching the resulting NotImplementedError here.
+    ``fit()`` routes strata/PSU/FPC designs through the method-specific
+    survey variance path — bootstrap (PR #355 weighted-FW + Rao-Wu),
+    placebo (stratified permutation + weighted-FW), or jackknife (PSU-
+    level LOO with stratum aggregation). The DGP's
+    ``survey_design_factory`` declares which methods are supported on
+    that specific DGP, so the caller skips unsupported methods entirely
+    rather than catching the resulting NotImplementedError / Case B-D
+    ValueError here.
     """
     try:
         with warnings.catch_warnings():

diff_diff/synthetic_did.py

@@ -1128,8 +1128,6 @@ def fit(  # type: ignore[override]
             treated_post_trajectory=treated_post_trajectory,
             time_weights_array=time_weights,
         )
-        self.results_._loo_unit_ids = loo_unit_ids
-        self.results_._loo_roles = loo_roles
         # Explicit LOO granularity flag for ``get_loo_effects_df``. The
         # non-survey and pweight-only jackknife paths run unit-level LOO
         # (one estimate per unit, matching ``control_unit_ids +
@@ -1143,6 +1141,18 @@ def fit(  # type: ignore[override]
             )
         else:
             self.results_._loo_granularity = None
+        # Only populate unit-level LOO bookkeeping when the granularity
+        # is actually unit-level (R7 P3). Leaving ``_loo_unit_ids`` /
+        # ``_loo_roles`` populated on the PSU path would cause
+        # ``_loo_unit_ids is not None`` availability checks (e.g.,
+        # ``practitioner.py`` / canned guidance) to call
+        # ``get_loo_effects_df()`` and hit ``NotImplementedError``.
+        if self.results_._loo_granularity == "unit":
+            self.results_._loo_unit_ids = loo_unit_ids
+            self.results_._loo_roles = loo_roles
+        else:
+            self.results_._loo_unit_ids = None
+            self.results_._loo_roles = None
         self.results_._fit_snapshot = fit_snapshot
 
         self._unit_weights = unit_weights