Merge branch 'main' into json_optional_data_path

causal_testing/json_front/json_class.py

@@ -234,9 +234,7 @@ def _execute_test_case(
         causal_test_engine, estimation_model = self._setup_test(
             causal_test_case, test, test["conditions"] if "conditions" in test else None
         )
-        causal_test_result = causal_test_engine.execute_test(
-            estimation_model, causal_test_case, estimate_type=causal_test_case.estimate_type
-        )
+        causal_test_result = causal_test_engine.execute_test(estimation_model, causal_test_case)
 
         test_passes = causal_test_case.expected_causal_effect.apply(causal_test_result)
 

causal_testing/testing/causal_test_engine.py

@@ -81,7 +81,6 @@ def execute_test_suite(self, test_suite: CausalTestSuite) -> list[CausalTestResu
 
             estimators = test_suite[edge]["estimators"]
             tests = test_suite[edge]["tests"]
-            estimate_type = test_suite[edge]["estimate_type"]
             results = {}
             for estimator_class in estimators:
                 causal_test_results = []
@@ -96,16 +95,14 @@ def execute_test_suite(self, test_suite: CausalTestSuite) -> list[CausalTestResu
                     )
                     if estimator.df is None:
                         estimator.df = self.scenario_execution_data_df
-                    causal_test_result = self._return_causal_test_results(estimate_type, estimator, test)
+                    causal_test_result = self._return_causal_test_results(estimator, test)
                     causal_test_results.append(causal_test_result)
 
                 results[estimator_class.__name__] = causal_test_results
             test_suite_results[edge] = results
         return test_suite_results
 
-    def execute_test(
-        self, estimator: type(Estimator), causal_test_case: CausalTestCase, estimate_type: str = "ate"
-    ) -> CausalTestResult:
+    def execute_test(self, estimator: type(Estimator), causal_test_case: CausalTestCase) -> CausalTestResult:
         """Execute a causal test case and return the causal test result.
 
         Test case execution proceeds with the following steps:
@@ -120,7 +117,6 @@ def execute_test(
 
         :param estimator: A reference to an Estimator class.
         :param causal_test_case: The CausalTestCase object to be tested
-        :param estimate_type: A string which denotes the type of estimate to return, ATE or CATE.
         :return causal_test_result: A CausalTestResult for the executed causal test case.
         """
         if self.scenario_execution_data_df.empty:
@@ -142,18 +138,17 @@ def execute_test(
         if self._check_positivity_violation(variables_for_positivity):
             raise ValueError("POSITIVITY VIOLATION -- Cannot proceed.")
 
-        causal_test_result = self._return_causal_test_results(estimate_type, estimator, causal_test_case)
+        causal_test_result = self._return_causal_test_results(estimator, causal_test_case)
         return causal_test_result
 
-    def _return_causal_test_results(self, estimate_type, estimator, causal_test_case):
+    def _return_causal_test_results(self, estimator, causal_test_case):
         """Depending on the estimator used, calculate the 95% confidence intervals and return in a causal_test_result
 
-        :param estimate_type: A string which denotes the type of estimate to return
         :param estimator: An Estimator class object
         :param causal_test_case: The concrete test case to be executed
         :return: a CausalTestResult object containing the confidence intervals
         """
-        if estimate_type == "cate":
+        if causal_test_case.estimate_type == "cate":
             logger.debug("calculating cate")
             if not hasattr(estimator, "estimate_cates"):
                 raise NotImplementedError(f"{estimator.__class__} has no CATE method.")
@@ -165,7 +160,7 @@ def _return_causal_test_results(self, estimate_type, estimator, causal_test_case
                 effect_modifier_configuration=causal_test_case.effect_modifier_configuration,
                 confidence_intervals=confidence_intervals,
             )
-        elif estimate_type == "risk_ratio":
+        elif causal_test_case.estimate_type == "risk_ratio":
             logger.debug("calculating risk_ratio")
             risk_ratio, confidence_intervals = estimator.estimate_risk_ratio()
             causal_test_result = CausalTestResult(
@@ -174,7 +169,7 @@ def _return_causal_test_results(self, estimate_type, estimator, causal_test_case
                 effect_modifier_configuration=causal_test_case.effect_modifier_configuration,
                 confidence_intervals=confidence_intervals,
             )
-        elif estimate_type == "coefficient":
+        elif causal_test_case.estimate_type == "coefficient":
             logger.debug("calculating coefficient")
             coefficient, confidence_intervals = estimator.estimate_unit_ate()
             causal_test_result = CausalTestResult(
@@ -183,7 +178,7 @@ def _return_causal_test_results(self, estimate_type, estimator, causal_test_case
                 effect_modifier_configuration=causal_test_case.effect_modifier_configuration,
                 confidence_intervals=confidence_intervals,
             )
-        elif estimate_type == "ate":
+        elif causal_test_case.estimate_type == "ate":
             logger.debug("calculating ate")
             ate, confidence_intervals = estimator.estimate_ate()
             causal_test_result = CausalTestResult(
@@ -194,7 +189,7 @@ def _return_causal_test_results(self, estimate_type, estimator, causal_test_case
             )
             # causal_test_result = CausalTestResult(minimal_adjustment_set, ate, confidence_intervals)
             # causal_test_result.apply_test_oracle_procedure(self.causal_test_case.expected_causal_effect)
-        elif estimate_type == "ate_calculated":
+        elif causal_test_case.estimate_type == "ate_calculated":
             logger.debug("calculating ate")
             ate, confidence_intervals = estimator.estimate_ate_calculated()
             causal_test_result = CausalTestResult(
@@ -206,7 +201,9 @@ def _return_causal_test_results(self, estimate_type, estimator, causal_test_case
             # causal_test_result = CausalTestResult(minimal_adjustment_set, ate, confidence_intervals)
             # causal_test_result.apply_test_oracle_procedure(self.causal_test_case.expected_causal_effect)
         else:
-            raise ValueError(f"Invalid estimate type {estimate_type}, expected 'ate', 'cate', or 'risk_ratio'")
+            raise ValueError(
+                f"Invalid estimate type {causal_test_case.estimate_type}, expected 'ate', 'cate', or 'risk_ratio'"
+            )
         return causal_test_result
 
     def _check_positivity_violation(self, variables_list):

docs/source/usage.rst

@@ -107,8 +107,7 @@ various information. Here, we simply assert that the observed result is (on aver
 
    causal_test_result = causal_test_engine.execute_test(
        estimator = estimation_model,
-       causal_test_case = causal_test_case,
-       estimate_type = "ate")
+       causal_test_case = causal_test_case)
    test_passes = causal_test_case.expected_causal_effect.apply(causal_test_result)
    assert test_passes, "Expected to see a positive change in y."
 

examples/covasim_/doubling_beta/example_beta.py

@@ -65,7 +65,7 @@ def doubling_beta_CATE_on_csv(
     )
 
     # Add squared terms for beta, since it has a quadratic relationship with cumulative infections
-    causal_test_result = causal_test_engine.execute_test(linear_regression_estimator, causal_test_case, "ate")
+    causal_test_result = causal_test_engine.execute_test(linear_regression_estimator, causal_test_case)
 
     # Repeat for association estimate (no adjustment)
     no_adjustment_linear_regression_estimator = LinearRegressionEstimator(
@@ -78,7 +78,7 @@ def doubling_beta_CATE_on_csv(
         formula="cum_infections ~ beta + np.power(beta, 2)",
     )
     association_test_result = causal_test_engine.execute_test(
-        no_adjustment_linear_regression_estimator, causal_test_case, "ate"
+        no_adjustment_linear_regression_estimator, causal_test_case
     )
 
     # Store results for plotting
@@ -110,7 +110,7 @@ def doubling_beta_CATE_on_csv(
             formula="cum_infections ~ beta + np.power(beta, 2) + avg_age + contacts",
         )
         counterfactual_causal_test_result = causal_test_engine.execute_test(
-            linear_regression_estimator, causal_test_case, "ate"
+            linear_regression_estimator, causal_test_case
         )
         results_dict["counterfactual"] = {
             "ate": counterfactual_causal_test_result.test_value.value,

examples/covasim_/vaccinating_elderly/example_vaccine.py

@@ -99,7 +99,7 @@ def test_experimental_vaccinate_elderly(runs_per_test_per_config: int = 30, verb
         )
 
         # 10. Execute test and save results in dict
-        causal_test_result = causal_test_engine.execute_test(linear_regression_estimator, causal_test_case, "ate")
+        causal_test_result = causal_test_engine.execute_test(linear_regression_estimator, causal_test_case)
         if verbose:
             logging.info("Causation:\n%s", causal_test_result)
         results_dict[outcome_variable.name]["ate"] = causal_test_result.test_value.value

examples/lr91/example_max_conductances.py

@@ -148,7 +148,7 @@ 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")
+    causal_test_result = causal_test_engine.execute_test(linear_regression_estimator, causal_test_case)
     logger.info("%s", causal_test_result)
     return causal_test_result.test_value.value, causal_test_result.confidence_intervals
 

examples/poisson-line-process/example_poisson_process.py

@@ -123,7 +123,7 @@ def causal_test_intensity_num_shapes(
         )
 
     # 10. Execute the test
-    causal_test_result = causal_test_engine.execute_test(estimator, causal_test_case, causal_test_case.estimate_type)
+    causal_test_result = causal_test_engine.execute_test(estimator, causal_test_case)
 
     return causal_test_result
 

tests/testing_tests/test_causal_test_engine.py

@@ -189,9 +189,8 @@ def test_execute_test_observational_linear_regression_estimator_coefficient(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="coefficient"
-        )
+        self.causal_test_case.estimate_type = "coefficient"
+        causal_test_result = self.causal_test_engine.execute_test(estimation_model, self.causal_test_case)
         self.assertEqual(int(causal_test_result.test_value.value), 0)
 
     def test_execute_test_observational_linear_regression_estimator_risk_ratio(self):
@@ -205,9 +204,8 @@ 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.causal_test_case.estimate_type = "risk_ratio"
+        causal_test_result = self.causal_test_engine.execute_test(estimation_model, self.causal_test_case)
         self.assertEqual(int(causal_test_result.test_value.value), 0)
 
     def test_invalid_estimate_type(self):
@@ -221,8 +219,9 @@ def test_invalid_estimate_type(self):
             "A",
             self.causal_test_engine.scenario_execution_data_df,
         )
+        self.causal_test_case.estimate_type = "invalid"
         with self.assertRaises(ValueError):
-            self.causal_test_engine.execute_test(estimation_model, self.causal_test_case, estimate_type="invalid")
+            self.causal_test_engine.execute_test(estimation_model, self.causal_test_case)
 
     def test_execute_test_observational_linear_regression_estimator_squared_term(self):
         """Check that executing the causal test case returns the correct results for dummy data with a squared term
@@ -258,9 +257,8 @@ def test_execute_observational_causal_forest_estimator_cates(self):
             self.causal_test_engine.scenario_execution_data_df,
             effect_modifiers={"M": None},
         )
-        causal_test_result = self.causal_test_engine.execute_test(
-            estimation_model, self.causal_test_case, estimate_type="cate"
-        )
+        self.causal_test_case.estimate_type = "cate"
+        causal_test_result = self.causal_test_engine.execute_test(estimation_model, self.causal_test_case)
         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]