def test_pickle_inferenceresult(self):
Y, T, X, W = TestInference.Y, TestInference.T, TestInference.X, TestInference.W
est = DML(model_y=LinearRegression(),
model_t=LinearRegression(),
model_final=Lasso(alpha=0.1, fit_intercept=False),
featurizer=PolynomialFeatures(degree=1, include_bias=False),
random_state=123)
est.fit(Y, T, X=X, W=W)
effect_inf = est.effect_inference(X)
s = pickle.dumps(effect_inf)
def test_inference_with_none_stderr(self):
Y, T, X, W = TestInference.Y, TestInference.T, TestInference.X, TestInference.W
est = DML(model_y=LinearRegression(),
model_t=LinearRegression(),
model_final=Lasso(alpha=0.1, fit_intercept=False),
featurizer=PolynomialFeatures(degree=1, include_bias=False),
random_state=123)
est.fit(Y, T, X=X, W=W)
est.summary()
est.coef__inference().summary_frame()
est.intercept__inference().summary_frame()
est.effect_inference(X).summary_frame()
est.effect_inference(X).population_summary()
est.const_marginal_effect_inference(X).summary_frame()
est.marginal_effect_inference(T, X).summary_frame()
est = NonParamDML(model_y=LinearRegression(),
model_t=LinearRegression(),
model_final=LinearRegression(fit_intercept=False),
featurizer=PolynomialFeatures(degree=1, include_bias=False),
random_state=123)
est.fit(Y, T, X=X, W=W)
est.effect_inference(X).summary_frame()
est.effect_inference(X).population_summary()
est.const_marginal_effect_inference(X).summary_frame()
est.marginal_effect_inference(T, X).summary_frame()
est = DRLearner(model_regression=LinearRegression(),
model_propensity=LogisticRegression(),
model_final=LinearRegression())
est.fit(Y, T, X=X, W=W)
est.effect_inference(X).summary_frame()
est.effect_inference(X).population_summary()
est.const_marginal_effect_inference(X).summary_frame()
est.marginal_effect_inference(T, X).summary_frame()