No mention of CausalTestEngine

Author: jmafoster1

causal_testing/testing/causal_test_adequacy.py

@@ -2,10 +2,10 @@
 This module contains code to measure various aspects of causal test adequacy.
 """
 from causal_testing.testing.causal_test_suite import CausalTestSuite
+from causal_testing.data_collection.data_collector import DataCollector
 from causal_testing.specification.causal_specification import CausalSpecification
 from causal_testing.testing.estimators import Estimator
 from causal_testing.testing.causal_test_case import CausalTestCase
-from causal_testing.testing.causal_test_engine import CausalTestEngine
 from itertools import combinations
 from copy import deepcopy
 from sklearn.model_selection import KFold
@@ -35,11 +35,11 @@ def measure_adequacy(self):
 
 class DataAdequacy:
     def __init__(
-        self, test_case: CausalTestCase, test_engine: CausalTestEngine, estimator: Estimator, bootstrap_size: int = 100
+        self, test_case: CausalTestCase, estimator: Estimator, data_collector: DataCollector, bootstrap_size: int = 100
     ):
         self.test_case = test_case
-        self.test_engine = test_engine
         self.estimator = estimator
+        self.data_collector = data_collector
         self.kurtosis = None
         self.outcomes = None
         self.bootstrap_size = bootstrap_size
@@ -50,7 +50,7 @@ def measure_adequacy(self):
             estimator = deepcopy(self.estimator)
             estimator.df = estimator.df.sample(len(estimator.df), replace=True, random_state=i)
             try:
-                results.append(self.test_engine.execute_test(estimator, self.test_case))
+                results.append(self.test.execute_test(estimator, self.data_collector))
             except np.LinAlgError:
                 continue
         outcomes = [self.test_case.expected_causal_effect.apply(c) for c in results]

causal_testing/testing/causal_test_case.py

@@ -18,12 +18,8 @@ class CausalTestCase:
     """
     A CausalTestCase extends the information held in a BaseTestCase. As well as storing the treatment and outcome
     variables, a CausalTestCase stores the values of these variables. Also the outcome variable and value are
-    specified.
-
-    The goal of a CausalTestCase is to test whether the intervention made to the control via the treatment causes the
-    model-under-test to produce the expected change. The CausalTestCase structure is designed for execution using the
-    CausalTestEngine, using either execute_test() function to execute a single test case or packing CausalTestCases into
-    a CausalTestSuite and executing them as a batch using the execute_test_suite() function.
+    specified. The goal of a CausalTestCase is to test whether the intervention made to the control via the treatment causes the
+    model-under-test to produce the expected change.
     """
 
     def __init__(

causal_testing/testing/causal_test_suite.py

@@ -22,7 +22,7 @@ class CausalTestSuite(UserDict):
     and the values are test objects. Test Objects hold a causal_test_case_list which is a list of causal_test_cases
     which provide control and treatment values, and an iterator of Estimator Class References
 
-    This dictionary can be fed into the CausalTestEngines execute_test_suite function which will iterate over all the
+    This dictionary can be fed into the execute_test_suite function which will iterate over all the
     base_test_case's and execute each causal_test_case with each iterator.
     """
 

tests/testing_tests/test_causal_test_case.py

@@ -60,9 +60,8 @@ def tearDown(self) -> None:
         remove_temp_dir_if_existent()
 
 
-
 class TestCausalTestExecution(unittest.TestCase):
-    """Test the CausalTestEngine workflow using observational data.
+    """Test the causal test execution workflow using observational data.
 
     The causal test engine (CTE) is the main workflow for the causal testing framework. The CTE takes a causal test case
     and a causal specification and computes the causal effect of the intervention on the outcome of interest.
@@ -246,12 +245,8 @@ def test_execute_observational_causal_forest_estimator_cates(self):
         """Check that executing the causal test case returns the correct conditional average treatment effects for
         dummy data with effect multiplicative effect modification. C ~ (4*(A+2) + D)*M"""
         # Add some effect modifier M that has a multiplicative effect on C
-        self.df["M"] = np.random.randint(
-            1, 5, len(self.df)
-        )
-        self.df["C"] *= self.df[
-            "M"
-        ]
+        self.df["M"] = np.random.randint(1, 5, len(self.df))
+        self.df["C"] *= self.df["M"]
         estimation_model = CausalForestEstimator(
             "A",
             self.treatment_value,
@@ -274,4 +269,4 @@ def test_execute_observational_causal_forest_estimator_cates(self):
         self.assertLess(causal_test_result_m3["cate"].mean(), causal_test_result_m4["cate"].mean())
 
     def tearDown(self) -> None:
-        remove_temp_dir_if_existent()
+        remove_temp_dir_if_existent()