Temporary draft PR to identify notebook test failure cause

econml/tests/test_causal_analysis.py

@@ -9,6 +9,7 @@
 from numpy.core.fromnumeric import squeeze
 import pandas as pd
 import pytest
+import gc
 
 from econml.solutions.causal_analysis import CausalAnalysis
 from econml.solutions.causal_analysis._causal_analysis import _CausalInsightsConstants
@@ -692,6 +693,9 @@ def test_random_state(self):
             np.testing.assert_equal(glo.point.values, glo2.point.values)
             np.testing.assert_equal(glo.stderr.values, glo2.stderr.values)
 
+            del ca, glo, ca2, glo2
+            gc.collect()
+
     def test_can_set_categories(self):
         y = pd.Series(np.random.choice([0, 1], size=(500,)))
         X = pd.DataFrame({'a': np.random.normal(size=500),
@@ -785,6 +789,9 @@ def test_invalid_inds(self):
                         self.assertEqual(ca.untrained_feature_indices_, [(4, 'cat_limit'),
                                                                          (5, 'cat_limit')])
 
+                    del ca
+                    gc.collect()
+
     # Add tests that guarantee that the reliance on DML feature order is not broken, such as
     # Creare a transformer that zeros out all variables after the first n_x variables, so it zeros out W
     # Pass an example where W is irrelevant and X is confounder