Fixed inference results when cate_feature_names is not defined

econml/inference.py

@@ -237,7 +237,7 @@ def coef__inference(self):
         if coef.size == 0:  # X is None
             raise AttributeError("X is None, please call intercept_inference to learn the constant!")
 
-        if callable(self._est.cate_feature_names):
+        if hasattr(self._est, 'cate_feature_names') and callable(self._est.cate_feature_names):
             def fname_transformer(x):
                 return self._est.cate_feature_names(x)
         else:
@@ -426,7 +426,7 @@ def coef__inference(self, T):
         coef_stderr = self.fitted_models_final[ind].coef_stderr_
         if coef.size == 0:  # X is None
             raise AttributeError("X is None, please call intercept_inference to learn the constant!")
-        if callable(self._est.cate_feature_names):
+        if hasattr(self._est, 'cate_feature_names') and callable(self._est.cate_feature_names):
             def fname_transformer(x):
                 return self._est.cate_feature_names(x)
         else:
@@ -692,7 +692,7 @@ def summary_frame(self, alpha=0.1, value=0, decimals=3, feat_name=None):
         if self.d_y == 1:
             res.index = res.index.droplevel(1)
         if self.inf_type == 'coefficient':
-            if feat_name and self.fname_transformer:
+            if feat_name is not None and self.fname_transformer:
                 ind = self.fname_transformer(feat_name)
             else:
                 ct = res.shape[0] // self.d_y

econml/tests/test_inference.py

@@ -0,0 +1,53 @@
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License.
+
+import numpy as np
+import unittest
+from sklearn.base import clone
+from sklearn.preprocessing import PolynomialFeatures
+from econml.dml import LinearDMLCateEstimator
+
+
+class TestInference(unittest.TestCase):
+
+    @classmethod
+    def setUpClass(cls):
+        np.random.seed(123)
+        # DGP constants
+        cls.n = 1000
+        cls.d_w = 30
+        cls.d_x = 5
+        # Generate data
+        cls.X = np.random.uniform(0, 1, size=(cls.n, cls.d_x))
+        cls.W = np.random.normal(0, 1, size=(cls.n, cls.d_w))
+        cls.T = np.random.normal(0, 1, size=(cls.n, ))
+        cls.Y = np.random.normal(0, 1, size=(cls.n, ))
+
+    def test_inference_results(self):
+        """Tests the inference results summary."""
+        # Test inference results when `cate_feature_names` doesn not exist
+        cate_est = LinearDMLCateEstimator(
+            featurizer=PolynomialFeatures(degree=1,
+                                          include_bias=False)
+        )
+        wrapped_est = self._NoFeatNamesEst(cate_est)
+        wrapped_est.fit(
+            TestInference.Y,
+            TestInference.T,
+            TestInference.X,
+            TestInference.W,
+            inference='statsmodels'
+        )
+        summary_results = wrapped_est.summary()
+        coef_rows = np.asarray(summary_results.tables[0].data)[1:, 0]
+        np.testing.assert_array_equal(coef_rows, ['X{}'.format(i) for i in range(TestInference.d_x)])
+
+    class _NoFeatNamesEst:
+        def __init__(self, cate_est):
+            self.cate_est = clone(cate_est, safe=False)
+
+        def __getattr__(self, name):
+            if name != 'cate_feature_names':
+                return getattr(self.cate_est, name)
+            else:
+                return self.__getattribute__(name)