Add warning on ratio sieve all-K fallback

Symmetric with the inverse propensity fallback: emit UserWarning when
no sieve candidate succeeds and ratio falls back to constant 1.
Updated test to assert the warning fires.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

Author: igerber

diff_diff/efficient_did_covariates.py

@@ -235,6 +235,16 @@ def estimate_propensity_ratio_sieve(
             best_ic = ic_val
             best_ratio = r_hat.copy()
 
+    # Warn if no sieve fit succeeded (falling back to constant ratio 1)
+    if best_ic == np.inf:
+        warnings.warn(
+            "Propensity ratio sieve estimation failed for all K values. "
+            "Falling back to constant ratio of 1 (no ratio adjustment). "
+            "The DR estimator relies on outcome regression only.",
+            UserWarning,
+            stacklevel=2,
+        )
+
     # Overlap diagnostics: warn if ratios require significant clipping
     n_extreme = int(np.sum((best_ratio < 1.0 / ratio_clip) | (best_ratio > ratio_clip)))
     if n_extreme > 0:

tests/test_efficient_did.py

@@ -1448,8 +1448,8 @@ def test_covariates_pt_all_bootstrap(self):
 class TestSieveFallbacks:
     """Tier 2: sieve estimation failure fallbacks."""
 
-    def test_ratio_sieve_fallback_tiny_group(self):
-        """When comparison group is too small for any basis, fall back to constant ratio."""
+    def test_ratio_sieve_fallback_tiny_group_warns(self):
+        """When comparison group is too small for any basis, fall back with warning."""
         from diff_diff.efficient_did_covariates import estimate_propensity_ratio_sieve
 
         rng = np.random.default_rng(42)
@@ -1460,9 +1460,11 @@ def test_ratio_sieve_fallback_tiny_group(self):
         # Tiny comparison group: only 2 units (fewer than any basis dimension)
         mask_gp = np.zeros(n, dtype=bool)
         mask_gp[50:52] = True
-        ratio = estimate_propensity_ratio_sieve(X, mask_g, mask_gp, k_max=3)
-        # Should produce valid ratios (fallback to constant 1)
+        with pytest.warns(UserWarning, match="Propensity ratio sieve estimation failed"):
+            ratio = estimate_propensity_ratio_sieve(X, mask_g, mask_gp, k_max=3)
         assert np.all(np.isfinite(ratio))
+        # Fallback: constant ratio of 1 (clipped to [1/ratio_clip, ratio_clip])
+        assert np.allclose(ratio, 1.0)
 
     def test_inverse_propensity_sieve_fallback_warns(self):
         """When group is too small for sieve, fall back with warning."""