Address twenty-fourth round of CI review findings on PR #318

P2 code quality (SDiD jackknife step-tag bug).

The SyntheticDiD practitioner step "Leave-one-out influence
(jackknife)" was tagged ``_step_name="sensitivity"``. DR's SDiD
native battery covers pre-treatment fit, weight concentration,
``in_time_placebo``, and ``sensitivity_to_zeta_omega`` — but not
the jackknife LOO workflow, which requires a separate
``variance_method='jackknife'`` fit before ``get_loo_effects_df``
returns anything. As soon as the native block ran,
``_collect_next_steps`` marked ``"sensitivity"`` complete and
suppressed the jackknife recommendation, overstating what the
report had actually executed. Same class as round-20 Hausman
(``heterogeneity`` -> ``parallel_trends``) and the pre-emptive
TROP-placebo retag.

Retag the step as ``_step_name="loo_jackknife"`` so it persists
regardless of which DR blocks ran. No DR check maps to the new
tag — the step stays in ``next_steps`` until the user completes
it explicitly.

P3 coverage. Add the two regressions the reviewer specified:

  * ``test_sdid_jackknife_step_persists_via_practitioner_filter``
    (unit-level) asserts ``practitioner_next_steps(sdid_stub,
    completed_steps=["sensitivity"])`` still surfaces the jackknife
    label;
  * ``test_sdid_jackknife_step_persists_in_dr_next_steps``
    (integration) asserts ``DiagnosticReport(sdid).to_dict()
    ["next_steps"]`` preserves the recommendation when only the
    default native SDiD diagnostics ran.

227 BR / DR / practitioner tests pass.

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

Author: igerber

diff_diff/practitioner.py

@@ -570,7 +570,16 @@ def _handle_synthetic(results: Any):
                 "          'with positive effective support.')"
             ),
             priority="medium",
-            step_name="sensitivity",
+            # DR's SyntheticDiD native battery covers pre-treatment fit,
+            # weight concentration, in-time placebo, and zeta-omega
+            # sensitivity, but NOT the jackknife LOO workflow (which
+            # requires a separate ``variance_method='jackknife'`` fit
+            # via ``get_loo_effects_df``). Tagging this recommendation
+            # as ``sensitivity`` caused ``_collect_next_steps`` to
+            # suppress it as soon as the native block ran, even though
+            # the jackknife was never executed. Round-24 P2 CI review
+            # on PR #318; same class as round-20 Hausman mistag.
+            step_name="loo_jackknife",
         ),
         _step(
             baker_step=6,

tests/test_business_report.py

@@ -2290,6 +2290,74 @@ def test_efficient_compare_control_groups_persists_after_sensitivity_runs(self):
         )
 
 
+class TestSDiDJackknifeStepPersistsAfterNativeSensitivity:
+    """Round-24 P2 CI review on PR #318: the SyntheticDiD practitioner
+    step "Leave-one-out influence (jackknife)" must persist after
+    ``DiagnosticReport`` marks ``sensitivity`` complete via the SDiD
+    native battery (pre-treatment fit, weight concentration,
+    ``in_time_placebo``, ``sensitivity_to_zeta_omega``). DR does NOT
+    run the jackknife LOO workflow — ``get_loo_effects_df`` requires a
+    separate ``variance_method='jackknife'`` fit — so suppressing the
+    recommendation when the native block fires overstates what the
+    report has already executed. Same class as round-20 Hausman and
+    pre-emptive TROP-placebo retags: step_name was coarser than DR's
+    actual coverage.
+    """
+
+    def test_sdid_jackknife_step_persists_via_practitioner_filter(self):
+        """Unit-level: ``practitioner_next_steps`` with
+        ``completed_steps=["sensitivity"]`` still surfaces the jackknife
+        recommendation because it is now tagged ``loo_jackknife``.
+        """
+        from diff_diff.practitioner import practitioner_next_steps
+
+        class SyntheticDiDResults:
+            pass
+
+        stub = SyntheticDiDResults()
+        stub.att = 1.0
+        stub.se = 0.2
+        stub.p_value = 0.001
+        stub.conf_int = (0.6, 1.4)
+        stub.alpha = 0.05
+        stub.n_obs = 200
+        stub.n_treated = 20
+        stub.n_control = 180
+        stub.survey_metadata = None
+        stub.event_study_effects = None
+
+        labels = [
+            s.get("label", "")
+            for s in practitioner_next_steps(
+                stub, completed_steps=["sensitivity"], verbose=False
+            )["next_steps"]
+        ]
+        assert any(
+            "Leave-one-out influence (jackknife)" in lab for lab in labels
+        ), (
+            "SDiD jackknife recommendation must persist after DR marks "
+            "sensitivity complete — the SDiD native battery does not run "
+            "the jackknife LOO workflow (requires a separate "
+            "variance_method='jackknife' fit)."
+        )
+
+    def test_sdid_jackknife_step_persists_in_dr_next_steps(self, sdid_fit):
+        """Integration: ``DiagnosticReport(...).to_dict()["next_steps"]``
+        preserves the jackknife recommendation when only the default
+        native SDiD diagnostics ran.
+        """
+        from diff_diff import DiagnosticReport
+
+        fit, _ = sdid_fit
+        next_steps = DiagnosticReport(fit).to_dict()["next_steps"]
+        labels = [s.get("label", "") for s in next_steps]
+        assert any("Leave-one-out influence (jackknife)" in lab for lab in labels), (
+            "DR next_steps must preserve the SDiD jackknife recommendation "
+            "when the SDiD native battery ran but the jackknife workflow "
+            f"did not. Got labels: {labels}"
+        )
+
+
 class TestTROPInTimePlaceboStepTaggedAsPlacebo:
     """Pre-emptive audit regression: the TROP practitioner workflow
     step "In-time or in-space placebo" was previously tagged