Updated docs, tests and examples

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]