All examples run

Author: jmafoster1

examples/covasim_/doubling_beta/causal_test_beta.py

@@ -50,18 +50,20 @@ def doubling_beta_CATE_on_csv(observational_data_path: str, simulate_counterfact
     linear_regression_estimator = LinearRegressionEstimator('beta', 0.032, 0.016,
                                                             {'avg_age', 'contacts'},  # We use custom adjustment set
                                                             'cum_infections',
-                                                            df=past_execution_df)
+                                                            df=past_execution_df,
+                                                            formula="cum_infections ~ beta + np.power(beta, 2) + avg_age + contacts")
 
     # Add squared terms for beta, since it has a quadratic relationship with cumulative infections
-    linear_regression_estimator.add_squared_term_to_df('beta')
+    # linear_regression_estimator.add_squared_term_to_df('beta')
     causal_test_result = causal_test_engine.execute_test(linear_regression_estimator, causal_test_case, 'ate')
 
     # Repeat for association estimate (no adjustment)
     no_adjustment_linear_regression_estimator = LinearRegressionEstimator('beta', 0.032, 0.016,
                                                                           set(),
                                                                           'cum_infections',
-                                                                          df=past_execution_df)
-    no_adjustment_linear_regression_estimator.add_squared_term_to_df('beta')
+                                                                          df=past_execution_df,
+                                                                          formula="cum_infections ~ beta + np.power(beta, 2)")
+    # no_adjustment_linear_regression_estimator.add_squared_term_to_df('beta')
     association_test_result = causal_test_engine.execute_test(no_adjustment_linear_regression_estimator, causal_test_case, 'ate')
 
     # Store results for plotting
@@ -82,8 +84,9 @@ def doubling_beta_CATE_on_csv(observational_data_path: str, simulate_counterfact
         counterfactual_linear_regression_estimator = LinearRegressionEstimator('beta', 0.032, 0.016,
                                                                                {'avg_age', 'contacts'},
                                                                                'cum_infections',
-                                                                               df=counterfactual_past_execution_df)
-        counterfactual_linear_regression_estimator.add_squared_term_to_df('beta')
+                                                                               df=counterfactual_past_execution_df,
+                                                                               formula="cum_infections ~ beta + np.power(beta, 2) + avg_age + contacts")
+        # 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.test_value.value,
                                           'cis': counterfactual_causal_test_result.confidence_intervals,

examples/poisson-line-process/.gitignore

@@ -0,0 +1,2 @@
+intensity_num_shapes_results_random_1000.csv
+width_num_shapes_results_random_1000.csv

examples/poisson-line-process/causal_test_poisson.py

@@ -117,20 +117,18 @@ def test_intensity_num_shapes(
             effect_modifiers=causal_test_case.effect_modifier_configuration,
         )
     else:
+        square_terms = [f"np.power({t}, 2)" for t in square_terms]
+        inverse_terms = [f"np.float_power({t}, -1)" for t in inverse_terms]
         estimator = LinearRegressionEstimator(
             treatment=treatment,
             control_value=causal_test_case.control_value,
             treatment_value=causal_test_case.treatment_value,
             adjustment_set=set(),
             outcome=outcome,
             df=data,
-            intercept=0,
             effect_modifiers=causal_test_case.effect_modifier_configuration,
+            formula=f"{outcome} ~ {treatment} + {'+'.join(square_terms + inverse_terms + list([e.name for e in causal_test_case.effect_modifier_configuration]))} -1"
         )
-        for t in square_terms:
-            estimator.add_squared_term_to_df(t)
-        for t in inverse_terms:
-            estimator.add_inverse_term_to_df(t)
 
     # 10. Execute the test
     causal_test_result = causal_test_engine.execute_test(

examples/poisson-line-process/intensity_num_shapes_results_random_1000.csv

@@ -7,5 +7,3 @@ To run this case study:
    (instructions are provided in the project README).
 2. Change directory to `causal_testing/examples/poisson`.
 3. Run the command `python run_causal_tests.py --data_path data.csv --dag_path dag.dot --json_path causal_tests.json`
-
-This should print a series of causal test results and produce two CSV files. `intensity_num_shapes_results_random_1000.csv` corresponds to table 1, and `width_num_shapes_results_random_1000.csv` relates to our findings regarding the relationship of width and `P_u`.