def test_all_kinds(self):
T = [1, 0, 1, 2, 0, 2] * 5
Y = [1, 2, 3, 4, 5, 6] * 5
X = np.array([1, 1, 2, 2, 1, 2] * 5).reshape(-1, 1)
est = LinearDML(n_splits=2)
for kind in ['percentile', 'pivot', 'normal']:
with self.subTest(kind=kind):
inference = BootstrapInference(n_bootstrap_samples=5,
bootstrap_type=kind)
est.fit(Y, T, inference=inference)
i = est.const_marginal_effect_interval()
inf = est.const_marginal_effect_inference()
assert i[0].shape == i[1].shape == inf.point_estimate.shape
assert np.allclose(i[0], inf.conf_int()[0])
assert np.allclose(i[1], inf.conf_int()[1])
est.fit(Y, T, X=X, inference=inference)
i = est.const_marginal_effect_interval(X)
inf = est.const_marginal_effect_inference(X)
assert i[0].shape == i[1].shape == inf.point_estimate.shape
assert np.allclose(i[0], inf.conf_int()[0])
assert np.allclose(i[1], inf.conf_int()[1])
i = est.coef__interval()
inf = est.coef__inference()
assert i[0].shape == i[1].shape == inf.point_estimate.shape
assert np.allclose(i[0], inf.conf_int()[0])
assert np.allclose(i[1], inf.conf_int()[1])
i = est.effect_interval(X)
inf = est.effect_inference(X)
assert i[0].shape == i[1].shape == inf.point_estimate.shape
assert np.allclose(i[0], inf.conf_int()[0])
assert np.allclose(i[1], inf.conf_int()[1])
def test_stratify(self):
"""Test that we can properly stratify by treatment"""
T = [1, 0, 1, 2, 0, 2]
Y = [1, 2, 3, 4, 5, 6]
X = np.array([1, 1, 2, 2, 1, 2]).reshape(-1, 1)
est = LinearDML(model_y=LinearRegression(),
model_t=LogisticRegression(),
discrete_treatment=True)
inference = BootstrapInference(n_bootstrap_samples=5)
est.fit(Y, T, inference=inference)
est.const_marginal_effect_interval()
est.fit(Y, T, X=X, inference=inference)
est.const_marginal_effect_interval(X)
est.fit(Y, np.asarray(T).reshape(-1, 1),
inference=inference) # test stratifying 2D treatment
est.const_marginal_effect_interval()