Use pandas inbuilt assert_series_equal test instead of casting everything to single values

Author: christopher-wild

tests/testing_tests/test_causal_test_case.py

@@ -118,7 +118,7 @@ def test_execute_test_observational_causal_forest_estimator(self):
             self.df,
         )
         causal_test_result = self.causal_test_case.execute_test(estimation_model, self.data_collector)
-        self.assertAlmostEqual(causal_test_result.test_value.value[0], 4, delta=1)
+        pd.testing.assert_series_equal(causal_test_result.test_value.value, pd.Series(4.0), atol=1)
 
     def test_invalid_causal_effect(self):
         """Check that executing the causal test case returns the correct results for dummy data using a linear
@@ -140,7 +140,7 @@ def test_execute_test_observational_linear_regression_estimator(self):
             self.df,
         )
         causal_test_result = self.causal_test_case.execute_test(estimation_model, self.data_collector)
-        self.assertAlmostEqual(causal_test_result.test_value.value[0], 4, delta=1e-10)
+        pd.testing.assert_series_equal(causal_test_result.test_value.value, pd.Series(4.0), atol=1e-10)
 
     def test_execute_test_observational_linear_regression_estimator_direct_effect(self):
         """Check that executing the causal test case returns the correct results for dummy data using a linear
@@ -167,7 +167,7 @@ def test_execute_test_observational_linear_regression_estimator_direct_effect(se
             self.df,
         )
         causal_test_result = causal_test_case.execute_test(estimation_model, self.data_collector)
-        self.assertAlmostEqual(causal_test_result.test_value.value[0], 4, delta=1e-10)
+        pd.testing.assert_series_equal(causal_test_result.test_value.value, pd.Series(4.0), atol=1e-10)
 
     def test_execute_test_observational_linear_regression_estimator_coefficient(self):
         """Check that executing the causal test case returns the correct results for dummy data using a linear
@@ -182,7 +182,7 @@ def test_execute_test_observational_linear_regression_estimator_coefficient(self
         )
         self.causal_test_case.estimate_type = "coefficient"
         causal_test_result = self.causal_test_case.execute_test(estimation_model, self.data_collector)
-        self.assertEqual(int(causal_test_result.test_value.value), 0)
+        pd.testing.assert_series_equal(causal_test_result.test_value.value, pd.Series({'D': 0.0}), atol=1e-1)
 
     def test_execute_test_observational_linear_regression_estimator_risk_ratio(self):
         """Check that executing the causal test case returns the correct results for dummy data using a linear
@@ -197,7 +197,7 @@ def test_execute_test_observational_linear_regression_estimator_risk_ratio(self)
         )
         self.causal_test_case.estimate_type = "risk_ratio"
         causal_test_result = self.causal_test_case.execute_test(estimation_model, self.data_collector)
-        self.assertEqual(int(causal_test_result.test_value.value), 0)
+        pd.testing.assert_series_equal(causal_test_result.test_value.value, pd.Series(0.0), atol=1)
 
     def test_invalid_estimate_type(self):
         """Check that executing the causal test case returns the correct results for dummy data using a linear
@@ -227,7 +227,7 @@ def test_execute_test_observational_linear_regression_estimator_squared_term(sel
             formula=f"C ~ A + {'+'.join(self.minimal_adjustment_set)} + (D ** 2)",
         )
         causal_test_result = self.causal_test_case.execute_test(estimation_model, self.data_collector)
-        self.assertAlmostEqual(round(causal_test_result.test_value.value[0], 1), 4, delta=1)
+        pd.testing.assert_series_equal(causal_test_result.test_value.value, pd.Series(4.0), atol=1)
 
     def test_execute_observational_causal_forest_estimator_cates(self):
         """Check that executing the causal test case returns the correct conditional average treatment effects for