Provide helpful error on missing inference methods

Author: kbattocchi

econml/bootstrap.py

@@ -154,6 +154,10 @@ def call(lower=5, upper=95):
                     return call_with_bounds(can_call, lower, upper)
                 return call
 
+        def get_inference():
+            raise NotImplementedError("The {0} method is not yet supported by bootstrap inference; "
+                                      "consider using a different inference method if available.".format(name))
+
         caught = None
         if self._compute_means and self._prefer_wrapped:
             try:
@@ -162,13 +166,20 @@ def call(lower=5, upper=95):
                 caught = err
             if name.endswith("_interval"):
                 return get_interval()
+            elif name.endswith("_inference"):
+                return get_inference()
         else:
             # try to get interval first if appropriate, since we don't prefer a wrapped method with this name
             if name.endswith("_interval"):
                 try:
                     return get_interval()
                 except AttributeError as err:
                     caught = err
+            if name.endswith("_inference"):
+                try:
+                    return get_inference()
+                except AttributeError as err:
+                    caught = err
             if self._compute_means:
                 return get_mean()
 

econml/tests/test_bootstrap.py

@@ -294,3 +294,15 @@ def test_stratify_orthoiv(self):
         inference = BootstrapInference(n_bootstrap_samples=20)
         est.fit(Y, T, Z, X=X, inference=inference)
         est.const_marginal_effect_interval(X)
+
+    def test_inference_throws_helpful_error(self):
+        """Test that we see that inference methods are not yet implemented"""
+        T = np.random.normal(size=(1000, 1))
+        Y = T + np.random.normal(size=(1000, 1))
+
+        opts = BootstrapInference(5, 2)
+
+        est = LinearDMLCateEstimator().fit(Y, T, inference=opts)
+
+        with self.assertRaises(NotImplementedError):
+            eff = est.const_marginal_effect_inference()