Merge pull request #257 from CITCOM-project/conditions

We now support placing conditions on the data again.

causal_testing/json_front/json_class.py

@@ -301,9 +301,6 @@ def _setup_test(self, causal_test_case: CausalTestCase, test: Mapping) -> Estima
         """Create the necessary inputs for a single test case
         :param causal_test_case: The concrete test case to be executed
         :param test: Single JSON test definition stored in a mapping (dict)
-        :param conditions: A list of conditions which should be applied to the
-        data. Conditions should be in the query format detailed at
-        https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.query.html
         :returns:
                 - estimation_model - Estimator instance for the test being run
         """
@@ -323,11 +320,13 @@ def _setup_test(self, causal_test_case: CausalTestCase, test: Mapping) -> Estima
             minimal_adjustment_set = minimal_adjustment_set - {causal_test_case.treatment_variable}
             estimator_kwargs["adjustment_set"] = minimal_adjustment_set
 
+        estimator_kwargs["query"] = test["query"] if "query" in test else ""
         estimator_kwargs["treatment"] = causal_test_case.treatment_variable.name
         estimator_kwargs["treatment_value"] = causal_test_case.treatment_value
         estimator_kwargs["control_value"] = causal_test_case.control_value
         estimator_kwargs["outcome"] = causal_test_case.outcome_variable.name
         estimator_kwargs["effect_modifiers"] = causal_test_case.effect_modifier_configuration
+        estimator_kwargs["df"] = self.data_collector.collect_data()
         estimator_kwargs["alpha"] = test["alpha"] if "alpha" in test else 0.05
 
         estimation_model = test["estimator"](**estimator_kwargs)

causal_testing/testing/causal_test_result.py

@@ -59,9 +59,10 @@ def push(s, inc="  "):
             f"Treatment value: {self.estimator.treatment_value}\n"
             f"Outcome: {self.estimator.outcome}\n"
             f"Adjustment set: {self.adjustment_set}\n"
-            f"Formula: {self.estimator.formula}\n"
-            f"{self.test_value.type}: {result_str}\n"
         )
+        if hasattr(self.estimator, "formula"):
+            base_str += f"Formula: {self.estimator.formula}\n"
+        base_str += f"{self.test_value.type}: {result_str}\n"
         confidence_str = ""
         if self.confidence_intervals:
             ci_str = " " + str(self.confidence_intervals)

causal_testing/testing/estimators.py

@@ -10,7 +10,7 @@
 import statsmodels.api as sm
 import statsmodels.formula.api as smf
 from econml.dml import CausalForestDML
-from patsy import dmatrix
+from patsy import dmatrix  # pylint: disable = no-name-in-module
 
 from sklearn.ensemble import GradientBoostingRegressor
 from statsmodels.regression.linear_model import RegressionResultsWrapper
@@ -50,21 +50,25 @@ def __init__(
         df: pd.DataFrame = None,
         effect_modifiers: dict[str:Any] = None,
         alpha: float = 0.05,
+        query: str = "",
     ):
         self.treatment = treatment
         self.treatment_value = treatment_value
         self.control_value = control_value
         self.adjustment_set = adjustment_set
         self.outcome = outcome
-        self.df = df
         self.alpha = alpha
+        self.df = df.query(query) if query else df
+
         if effect_modifiers is None:
             self.effect_modifiers = {}
         elif isinstance(effect_modifiers, dict):
             self.effect_modifiers = effect_modifiers
         else:
             raise ValueError(f"Unsupported type for effect_modifiers {effect_modifiers}. Expected iterable")
         self.modelling_assumptions = []
+        if query:
+            self.modelling_assumptions.append(query)
         self.add_modelling_assumptions()
         logger.debug("Effect Modifiers: %s", self.effect_modifiers)
 
@@ -100,8 +104,18 @@ def __init__(
         df: pd.DataFrame = None,
         effect_modifiers: dict[str:Any] = None,
         formula: str = None,
+        query: str = "",
     ):
-        super().__init__(treatment, treatment_value, control_value, adjustment_set, outcome, df, effect_modifiers)
+        super().__init__(
+            treatment=treatment,
+            treatment_value=treatment_value,
+            control_value=control_value,
+            adjustment_set=adjustment_set,
+            outcome=outcome,
+            df=df,
+            effect_modifiers=effect_modifiers,
+            query=query,
+        )
 
         self.model = None
 
@@ -116,13 +130,13 @@ def add_modelling_assumptions(self):
         Add modelling assumptions to the estimator. This is a list of strings which list the modelling assumptions that
         must hold if the resulting causal inference is to be considered valid.
         """
-        self.modelling_assumptions += (
+        self.modelling_assumptions.append(
             "The variables in the data must fit a shape which can be expressed as a linear"
             "combination of parameters and functions of variables. Note that these functions"
             "do not need to be linear."
         )
-        self.modelling_assumptions += "The outcome must be binary."
-        self.modelling_assumptions += "Independently and identically distributed errors."
+        self.modelling_assumptions.append("The outcome must be binary.")
+        self.modelling_assumptions.append("Independently and identically distributed errors.")
 
     def _run_logistic_regression(self, data) -> RegressionResultsWrapper:
         """Run logistic regression of the treatment and adjustment set against the outcome and return the model.
@@ -291,9 +305,18 @@ def __init__(
         effect_modifiers: dict[Variable:Any] = None,
         formula: str = None,
         alpha: float = 0.05,
+        query: str = "",
     ):
         super().__init__(
-            treatment, treatment_value, control_value, adjustment_set, outcome, df, effect_modifiers, alpha=alpha
+            treatment,
+            treatment_value,
+            control_value,
+            adjustment_set,
+            outcome,
+            df,
+            effect_modifiers,
+            alpha=alpha,
+            query=query,
         )
 
         self.model = None
@@ -314,7 +337,7 @@ def add_modelling_assumptions(self):
         Add modelling assumptions to the estimator. This is a list of strings which list the modelling assumptions that
         must hold if the resulting causal inference is to be considered valid.
         """
-        self.modelling_assumptions += (
+        self.modelling_assumptions.append(
             "The variables in the data must fit a shape which can be expressed as a linear"
             "combination of parameters and functions of variables. Note that these functions"
             "do not need to be linear."
@@ -509,8 +532,20 @@ def __init__(
         df: pd.DataFrame = None,
         intercept: int = 1,
         effect_modifiers: dict = None,  # Not used (yet?). Needed for compatibility
+        alpha: float = 0.05,
+        query: str = "",
     ):
-        super().__init__(treatment, treatment_value, control_value, adjustment_set, outcome, df, None)
+        super().__init__(
+            treatment=treatment,
+            treatment_value=treatment_value,
+            control_value=control_value,
+            adjustment_set=adjustment_set,
+            outcome=outcome,
+            df=df,
+            effect_modifiers=None,
+            alpha=alpha,
+            query=query,
+        )
         self.intercept = intercept
         self.model = None
         self.instrument = instrument
@@ -520,13 +555,17 @@ def add_modelling_assumptions(self):
         Add modelling assumptions to the estimator. This is a list of strings which list the modelling assumptions that
         must hold if the resulting causal inference is to be considered valid.
         """
-        self.modelling_assumptions += """The instrument and the treatment, and the treatment and the outcome must be
+        self.modelling_assumptions.append(
+            """The instrument and the treatment, and the treatment and the outcome must be
         related linearly in the form Y = aX + b."""
-        self.modelling_assumptions += """The three IV conditions must hold
+        )
+        self.modelling_assumptions.append(
+            """The three IV conditions must hold
             (i) Instrument is associated with treatment
             (ii) Instrument does not affect outcome except through its potential effect on treatment
             (iii) Instrument and outcome do not share causes
         """
+        )
 
     def estimate_iv_coefficient(self, df):
         """
@@ -569,7 +608,7 @@ def add_modelling_assumptions(self):
 
         :return self: Update self.modelling_assumptions
         """
-        self.modelling_assumptions += "Non-parametric estimator: no restrictions imposed on the data."
+        self.modelling_assumptions.append("Non-parametric estimator: no restrictions imposed on the data.")
 
     def estimate_ate(self) -> float:
         """Estimate the average treatment effect.

tests/testing_tests/test_estimators.py

@@ -125,16 +125,14 @@ def test_ate_adjustment(self):
         logistic_regression_estimator = LogisticRegressionEstimator(
             "length_in", 65, 55, {"large_gauge"}, "completed", df
         )
-        ate, _ = logistic_regression_estimator.estimate_ate(adjustment_config = {"large_gauge": 0})
+        ate, _ = logistic_regression_estimator.estimate_ate(adjustment_config={"large_gauge": 0})
         self.assertEqual(round(ate, 4), -0.3388)
 
     def test_ate_invalid_adjustment(self):
         df = self.scarf_df.copy()
         logistic_regression_estimator = LogisticRegressionEstimator("length_in", 65, 55, {}, "completed", df)
         with self.assertRaises(ValueError):
-            ate, _ = logistic_regression_estimator.estimate_ate(
-                adjustment_config = {"large_gauge": 0}
-            )
+            ate, _ = logistic_regression_estimator.estimate_ate(adjustment_config={"large_gauge": 0})
 
     def test_ate_effect_modifiers(self):
         df = self.scarf_df.copy()
@@ -216,6 +214,13 @@ def setUpClass(cls) -> None:
         cls.nhefs_df = load_nhefs_df()
         cls.chapter_11_df = load_chapter_11_df()
 
+    def test_query(self):
+        df = self.nhefs_df
+        linear_regression_estimator = LinearRegressionEstimator(
+            "treatments", None, None, set(), "outcomes", df, query="sex==1"
+        )
+        self.assertTrue(linear_regression_estimator.df.sex.all())
+
     def test_program_11_2(self):
         """Test whether our linear regression implementation produces the same results as program 11.2 (p. 141)."""
         df = self.chapter_11_df
@@ -395,7 +400,7 @@ def test_program_15_no_interaction_ate_calculated(self):
         # for term_to_square in terms_to_square:
 
         ate, [ci_low, ci_high] = linear_regression_estimator.estimate_ate_calculated(
-            adjustment_config = {k: self.nhefs_df.mean()[k] for k in covariates}
+            adjustment_config={k: self.nhefs_df.mean()[k] for k in covariates}
         )
         self.assertEqual(round(ate, 1), 3.5)
         self.assertEqual([round(ci_low, 1), round(ci_high, 1)], [1.9, 5])