Fix failing tests + missed changes throughout the framework

Author: rsomers1998

causal_testing/json_front/json_class.py

@@ -177,10 +177,10 @@ def _execute_test_case(self, causal_test_case: CausalTestCase, estimator: Estima
         result_string = str()
         if causal_test_result.ci_low() and causal_test_result.ci_high():
             result_string = (
-                f"{causal_test_result.ci_low()} < {causal_test_result.ate} <  {causal_test_result.ci_high()}"
+                f"{causal_test_result.ci_low()} < {causal_test_result.test_value.value} <  {causal_test_result.ci_high()}"
             )
         else:
-            result_string = causal_test_result.ate
+            result_string = causal_test_result.test_value.value
         if f_flag:
             assert test_passes, (
                 f"{causal_test_case}\n    FAILED - expected {causal_test_case.expected_causal_effect}, "
@@ -191,7 +191,7 @@ def _execute_test_case(self, causal_test_case: CausalTestCase, estimator: Estima
             logger.warning(
                 "   FAILED- expected %s, got %s",
                 causal_test_case.expected_causal_effect,
-                causal_test_result.ate,
+                causal_test_result.test_value.value,
             )
         return failed
 

causal_testing/testing/causal_test_outcome.py

@@ -71,7 +71,7 @@ class NoEffect(CausalTestOutcome):
 
     def apply(self, res: CausalTestResult) -> bool:
         if res.test_value.type == "ate":
-            return (res.ci_low() < 0 < res.ci_high()) or (abs(res.ate) < 1e-10)
+            return (res.ci_low() < 0 < res.ci_high()) or (abs(res.test_value.value) < 1e-10)
         elif res.test_value.type == "risk_ratio":
             return (res.ci_low() < 1 < res.ci_high()) or np.isclose(res.test_value.value, 1.0, atol=1e-10)
 

examples/covasim_/doubling_beta/causal_test_beta.py

@@ -64,10 +64,10 @@ def doubling_beta_CATE_on_csv(observational_data_path: str, simulate_counterfact
     association_test_result = causal_test_engine.execute_test(no_adjustment_linear_regression_estimator, causal_test_case, 'ate')
 
     # Store results for plotting
-    results_dict['association'] = {'ate': association_test_result.ate,
+    results_dict['association'] = {'ate': association_test_result.test_value.value,
                                    'cis': association_test_result.confidence_intervals,
                                    'df': past_execution_df}
-    results_dict['causation'] = {'ate': causal_test_result.ate,
+    results_dict['causation'] = {'ate': causal_test_result.test_value.value,
                                  'cis': causal_test_result.confidence_intervals,
                                  'df': past_execution_df}
 
@@ -84,7 +84,7 @@ def doubling_beta_CATE_on_csv(observational_data_path: str, simulate_counterfact
                                                                                df=counterfactual_past_execution_df)
         counterfactual_linear_regression_estimator.add_squared_term_to_df('beta')
         counterfactual_causal_test_result = causal_test_engine.execute_test(linear_regression_estimator, causal_test_case, 'ate')
-        results_dict['counterfactual'] = {'ate': counterfactual_causal_test_result.ate,
+        results_dict['counterfactual'] = {'ate': counterfactual_causal_test_result.test_value.value,
                                           'cis': counterfactual_causal_test_result.confidence_intervals,
                                           'df': counterfactual_past_execution_df}
         if verbose:

examples/covasim_/vaccinating_elderly/causal_test_vaccine.py

@@ -91,7 +91,7 @@ def experimental_causal_test_vaccinate_elderly(runs_per_test_per_config: int = 3
         causal_test_result = causal_test_engine.execute_test(linear_regression_estimator, causal_test_case, 'ate')
         if verbose:
             print(f"Causation:\n{causal_test_result}")
-        results_dict[outcome_variable.name]['ate'] = causal_test_result.ate
+        results_dict[outcome_variable.name]['ate'] = causal_test_result.test_value.value
         results_dict[outcome_variable.name]['cis'] = causal_test_result.confidence_intervals
         results_dict[outcome_variable.name]['test_passes'] = causal_test_case.expected_causal_effect.apply(
             causal_test_result)

examples/lr91/causal_test_max_conductances.py

@@ -132,7 +132,7 @@ def effects_on_APD90(observational_data_path, treatment_var, control_val, treatm
     # 10. Run the causal test and print results
     causal_test_result = causal_test_engine.execute_test(linear_regression_estimator, causal_test_case, 'ate')
     print(causal_test_result)
-    return causal_test_result.ate, causal_test_result.confidence_intervals
+    return causal_test_result.test_value.value, causal_test_result.confidence_intervals
 
 
 def plot_ates_with_cis(results_dict: dict, xs: list, save: bool = True):

examples/poisson-line-process/causal_test_poisson.py

@@ -182,8 +182,8 @@ def test_intensity_num_shapes(
             "height": wh,
             "control": control_value,
             "treatment": treatment_value,
-            "smt_risk_ratio": smt_causal_test_result.ate,
-            "obs_risk_ratio": obs_causal_test_result.ate,
+            "smt_risk_ratio": smt_causal_test_result.test_value.value,
+            "obs_risk_ratio": obs_causal_test_result.test_value.value,
         }
         intensity_num_shapes_results.append(results)
 

examples/poisson/run_causal_tests.py

@@ -70,8 +70,8 @@ def apply(self, res: CausalTestResult) -> bool:
         i = effect_modifier_configuration['intensity']
         self.i2c = i * 2 * c
         print("2ic:", f"2*{i}*{c}={self.i2c}")
-        print("ate:", res.ate)
-        return np.isclose(res.ate, self.i2c, atol=self.tolerance)
+        print("ate:", res.test_value.value)
+        return np.isclose(res.test_value.value, self.i2c, atol=self.tolerance)
 
     def __str__(self):
         if self.i2c is None:

tests/testing_tests/test_causal_test_engine.py

@@ -128,7 +128,7 @@ def test_execute_test_observational_causal_forest_estimator(self):
                                                  ('C',),
                                                  self.causal_test_engine.scenario_execution_data_df)
         causal_test_result = self.causal_test_engine.execute_test(estimation_model, self.causal_test_case)
-        self.assertAlmostEqual(causal_test_result.ate, 4, delta=1)
+        self.assertAlmostEqual(causal_test_result.test_value.value, 4, delta=1)
 
 
     def test_invalid_causal_effect(self):
@@ -159,7 +159,7 @@ def test_execute_test_observational_linear_regression_estimator(self):
                                                      ('C',),
                                                      self.causal_test_engine.scenario_execution_data_df)
         causal_test_result = self.causal_test_engine.execute_test(estimation_model, self.causal_test_case)
-        self.assertAlmostEqual(causal_test_result.ate, 4, delta=1e-10)
+        self.assertAlmostEqual(causal_test_result.test_value.value, 4, delta=1e-10)
 
 
     def test_execute_test_observational_linear_regression_estimator_direct_effect(self):
@@ -189,7 +189,7 @@ def test_execute_test_observational_linear_regression_estimator_direct_effect(se
                                                      ('C',),
                                                      causal_test_engine.scenario_execution_data_df)
         causal_test_result = causal_test_engine.execute_test(estimation_model, causal_test_case)
-        self.assertAlmostEqual(causal_test_result.ate, 0, delta=1e-10)
+        self.assertAlmostEqual(causal_test_result.test_value.value, 0, delta=1e-10)
 
     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
@@ -201,7 +201,7 @@ def test_execute_test_observational_linear_regression_estimator_risk_ratio(self)
                                                      ('A',),
                                                      self.causal_test_engine.scenario_execution_data_df)
         causal_test_result = self.causal_test_engine.execute_test(estimation_model, self.causal_test_case, estimate_type="risk_ratio")
-        self.assertEqual(int(causal_test_result.ate), 0)
+        self.assertEqual(int(causal_test_result.test_value.value), 0)
 
 
     def test_invalid_estimate_type(self):
@@ -227,7 +227,7 @@ def test_execute_test_observational_linear_regression_estimator_squared_term(sel
                                                      self.causal_test_engine.scenario_execution_data_df)
         estimation_model.add_squared_term_to_df('D')
         causal_test_result = self.causal_test_engine.execute_test(estimation_model, self.causal_test_case)
-        self.assertAlmostEqual(round(causal_test_result.ate, 1), 4, delta=1)
+        self.assertAlmostEqual(round(causal_test_result.test_value.value, 1), 4, delta=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
@@ -245,7 +245,7 @@ def test_execute_observational_causal_forest_estimator_cates(self):
                                                  self.causal_test_engine.scenario_execution_data_df,
                                                  effect_modifiers={Input('M', int): None})
         causal_test_result = self.causal_test_engine.execute_test(estimation_model, self.causal_test_case, estimate_type='cate')
-        causal_test_result = causal_test_result.ate
+        causal_test_result = causal_test_result.test_value.value
         # Check that each effect modifier's strata has a greater ATE than the last (ascending order)
         causal_test_result_m1 = causal_test_result.loc[causal_test_result['M'] == 1]
         causal_test_result_m2 = causal_test_result.loc[causal_test_result['M'] == 2]