def test_drlearners(self):
X = TestPandasIntegration.df[TestPandasIntegration.features]
W = TestPandasIntegration.df[TestPandasIntegration.controls]
Y = TestPandasIntegration.df[TestPandasIntegration.outcome]
T = TestPandasIntegration.df[TestPandasIntegration.bin_treat]
# Test LinearDRLearner
est = LinearDRLearner(model_propensity=GradientBoostingClassifier(),
model_regression=GradientBoostingRegressor())
est.fit(Y, T, X=X, W=W, inference='statsmodels')
treatment_effects = est.effect(X)
lb, ub = est.effect_interval(X, alpha=0.05)
self._check_input_names(est.summary(T=1))
self._check_popsum_names(est.effect_inference(X).population_summary())
# Test SparseLinearDRLearner
est = SparseLinearDRLearner(
model_propensity=GradientBoostingClassifier(),
model_regression=GradientBoostingRegressor())
est.fit(Y, T, X=X, W=W, inference='debiasedlasso')
treatment_effects = est.effect(X)
lb, ub = est.effect_interval(X, alpha=0.05)
self._check_input_names(est.summary(T=1))
self._check_popsum_names(est.effect_inference(X).population_summary())
# Test ForestDRLearner
est = ForestDRLearner(model_propensity=GradientBoostingClassifier(),
model_regression=GradientBoostingRegressor())
est.fit(Y, T, X=X, W=W, inference='blb')
treatment_effects = est.effect(X)
lb, ub = est.effect_interval(X, alpha=0.05)
self._check_popsum_names(est.effect_inference(X).population_summary())
def test_summary_discrete(self):
"""Tests the inference results summary for discrete treatment estimators."""
# Test inference results when `cate_feature_names` doesn not exist
for inference in [
BootstrapInference(n_bootstrap_samples=5), 'statsmodels'
]:
cate_est = LinearDRLearner(model_regression=LinearRegression(),
model_propensity=LogisticRegression(),
featurizer=PolynomialFeatures(
degree=2, include_bias=False))
cate_est.fit(TestInference.Y,
TestInference.T,
TestInference.X,
TestInference.W,
inference=inference)
summary_results = cate_est.summary(T=1)
coef_rows = np.asarray(summary_results.tables[0].data)[1:, 0]
fnames = PolynomialFeatures(degree=2, include_bias=False).fit(
TestInference.X).get_feature_names()
np.testing.assert_array_equal(coef_rows, fnames)
intercept_rows = np.asarray(summary_results.tables[1].data)[1:, 0]
np.testing.assert_array_equal(intercept_rows, ['intercept'])
cate_est = LinearDRLearner(model_regression=LinearRegression(),
model_propensity=LogisticRegression(),
featurizer=PolynomialFeatures(
degree=2, include_bias=False))
cate_est.fit(TestInference.Y,
TestInference.T,
TestInference.X,
TestInference.W,
inference=inference)
fnames = ['Q' + str(i) for i in range(TestInference.d_x)]
summary_results = cate_est.summary(T=1, feat_name=fnames)
coef_rows = np.asarray(summary_results.tables[0].data)[1:, 0]
fnames = PolynomialFeatures(degree=2, include_bias=False).fit(
TestInference.X).get_feature_names(input_features=fnames)
np.testing.assert_array_equal(coef_rows, fnames)
cate_est = LinearDRLearner(model_regression=LinearRegression(),
model_propensity=LogisticRegression(),
featurizer=None)
cate_est.fit(TestInference.Y,
TestInference.T,
TestInference.X,
TestInference.W,
inference=inference)
summary_results = cate_est.summary(T=1)
coef_rows = np.asarray(summary_results.tables[0].data)[1:, 0]
np.testing.assert_array_equal(
coef_rows, ['X' + str(i) for i in range(TestInference.d_x)])
cate_est = LinearDRLearner(model_regression=LinearRegression(),
model_propensity=LogisticRegression(),
featurizer=None)
cate_est.fit(TestInference.Y,
TestInference.T,
TestInference.X,
TestInference.W,
inference=inference)
fnames = ['Q' + str(i) for i in range(TestInference.d_x)]
summary_results = cate_est.summary(T=1, feat_name=fnames)
coef_rows = np.asarray(summary_results.tables[0].data)[1:, 0]
np.testing.assert_array_equal(coef_rows, fnames)
cate_est = LinearDRLearner(model_regression=LinearRegression(),
model_propensity=LogisticRegression(),
featurizer=None)
wrapped_est = self._NoFeatNamesEst(cate_est)
wrapped_est.fit(TestInference.Y,
TestInference.T,
TestInference.X,
TestInference.W,
inference=inference)
summary_results = wrapped_est.summary(T=1)
coef_rows = np.asarray(summary_results.tables[0].data)[1:, 0]
np.testing.assert_array_equal(
coef_rows, ['X' + str(i) for i in range(TestInference.d_x)])
cate_est = LinearDRLearner(model_regression=LinearRegression(),
model_propensity=LogisticRegression(),
featurizer=None)
wrapped_est = self._NoFeatNamesEst(cate_est)
wrapped_est.fit(TestInference.Y,
TestInference.T,
TestInference.X,
TestInference.W,
inference=inference)
fnames = ['Q' + str(i) for i in range(TestInference.d_x)]
summary_results = wrapped_est.summary(T=1, feat_name=fnames)
coef_rows = np.asarray(summary_results.tables[0].data)[1:, 0]
np.testing.assert_array_equal(coef_rows, fnames)