def test_dml_predict(self):
"""Testing dml.predict."""
np.random.seed(123)
# How many samples
n_samples = 200
# How many control features
n_cov = 5
# How many treatment variables
n_treatments = 10
for exp in range(100):
# Coefficients of how controls affect treatments
Alpha = 20 * np.random.rand(n_cov, n_treatments) - 10
# Coefficients of how controls affect outcome
beta = 20 * np.random.rand(n_cov) - 10
# Treatment effects that we want to estimate
effect = 20 * np.random.rand(n_treatments) - 10
y, T, X, epsilon = dgp.dgp_perfect_data_multiple_treatments(
n_samples, n_cov, n_treatments, Alpha, beta, effect)
# Run dml estimation
reg = dml.DMLCateEstimator(
np.arange(X.shape[1]), [],
np.arange(X.shape[1], X.shape[1] + T.shape[1]))
reg.fit(np.concatenate((X, T), axis=1), y)
y, T, X = dgp.dgp_perfect_counterfactual_data_multiple_treatments(
n_samples, n_cov, beta, effect, np.ones(n_treatments))
r2score = reg.score(np.concatenate((X, T), axis=1), y)
self.assertTrue(r2score > 0.99, "core.double_ml() wrong")
def test_dml_effect(self):
"""Testing dml.effect."""
np.random.seed(123)
# How many samples
n_samples = 200
# How many control features
n_cov = 5
# How many treatment variables
n_treatments = 10
for exp in range(100):
# Coefficients of how controls affect treatments
Alpha = 20 * np.random.rand(n_cov, n_treatments) - 10
# Coefficients of how controls affect outcome
beta = 20 * np.random.rand(n_cov) - 10
# Treatment effects that we want to estimate
effect = 20 * np.random.rand(n_treatments) - 10
y, T, X, epsilon = dgp.dgp_perfect_data_multiple_treatments(
n_samples, n_cov, n_treatments, Alpha, beta, effect)
# Run dml estimation
reg = dml.DMLCateEstimator(
np.arange(X.shape[1]), [],
np.arange(X.shape[1], X.shape[1] + T.shape[1]))
reg.fit(np.concatenate((X, T), axis=1), y)
T0 = np.zeros((1, T.shape[1]))
T1 = np.zeros((1, T.shape[1]))
dml_coef = np.zeros(T.shape[1])
for t in range(T.shape[1]):
T1[:, t] = 1
dml_coef[t] = reg.effect([], T0, T1)
T1[:, t] = 0
self.assertTrue(
np.max(np.abs(dml_coef - effect)) < 0.0000000001,
"core.double_ml() wrong")