def test_can_use_sample_weights(self):
"""
TODO Almost identical to DML test, so consider merging
Test that we can pass sample weights to an estimator.
"""
dml = LinearDRLearner(model_regression=LinearRegression(),
model_propensity=LogisticRegression(C=1000, solver='lbfgs', multi_class='auto'),
featurizer=FunctionTransformer(validate=True))
dml.fit(np.array([1, 2, 1, 2]), np.array([1, 2, 1, 2]), W=np.ones((4, 1)),
sample_weight=np.ones((4, )))
self.assertAlmostEqual(dml.intercept_(T=2), 1)
def test_can_use_vectors(self):
"""
TODO Almost identical to DML test, so consider merging
Test that we can pass vectors for T and Y (not only 2-dimensional arrays).
"""
dml = LinearDRLearner(model_regression=LinearRegression(),
model_propensity=LogisticRegression(C=1000, solver='lbfgs', multi_class='auto'),
fit_cate_intercept=False,
featurizer=FunctionTransformer(validate=True))
dml.fit(np.array([1, 2, 1, 2]), np.array([1, 2, 1, 2]), X=np.ones((4, 1)))
self.assertAlmostEqual(dml.coef_(T=2).reshape(())[()], 1)
def test_can_use_statsmodel_inference(self):
"""
TODO Almost identical to DML test, so consider merging
Test that we can use statsmodels to generate confidence intervals
"""
dml = LinearDRLearner(model_regression=LinearRegression(),
model_propensity=LogisticRegression(
C=1000, solver='lbfgs', multi_class='auto'))
dml.fit(np.array([2, 3, 1, 3, 2, 1, 1, 1]),
np.array([3, 2, 1, 2, 3, 1, 1, 1]),
np.ones((8, 1)),
inference='statsmodels')
interval = dml.effect_interval(np.ones((9, 1)),
T0=np.array([1, 1, 1, 1, 1, 1, 1, 1,
1]),
T1=np.array([2, 2, 3, 2, 2, 3, 2, 2,
3]),
alpha=0.05)
point = dml.effect(np.ones((9, 1)),
T0=np.array([1, 1, 1, 1, 1, 1, 1, 1, 1]),
T1=np.array([2, 2, 3, 2, 2, 3, 2, 2, 3]))
assert len(interval) == 2
lo, hi = interval
assert lo.shape == hi.shape == point.shape
assert (lo <= point).all()
assert (point <= hi).all()
assert (lo < hi).any(
) # for at least some of the examples, the CI should have nonzero width
interval = dml.const_marginal_effect_interval(np.ones((9, 1)),
alpha=0.05)
point = dml.const_marginal_effect(np.ones((9, 1)))
assert len(interval) == 2
lo, hi = interval
assert lo.shape == hi.shape == point.shape
assert (lo <= point).all()
assert (point <= hi).all()
assert (lo < hi).any(
) # for at least some of the examples, the CI should have nonzero width
interval = dml.coef__interval(T=2, alpha=0.05)
point = dml.coef_(T=2)
assert len(interval) == 2
lo, hi = interval
assert lo.shape == hi.shape == point.shape
assert (lo <= point).all()
assert (point <= hi).all()
assert (lo < hi).any(
) # for at least some of the examples, the CI should have nonzero width
def test_drlearner(self):
y, T, X, W = self._get_data()
for est in [LinearDRLearner(random_state=123),
SparseLinearDRLearner(random_state=123)]:
est.fit(y, T, X=X, W=W, cache_values=True)
np.testing.assert_equal(est.model_regression, 'auto')
est.model_regression = LinearRegression()
est.model_propensity = LogisticRegression()
est.fit(y, T, X=X, W=W, cache_values=True)
assert isinstance(est.model_regression, LinearRegression)
with pytest.raises(ValueError):
est.multitask_model_final = True
with pytest.raises(ValueError):
est.model_final = LinearRegression()
est.min_propensity = .1
est.mc_iters = 2
est.featurizer = PolynomialFeatures(degree=2, include_bias=False)
est.refit_final()
assert isinstance(est.featurizer_, PolynomialFeatures)
np.testing.assert_equal(est.mc_iters, 2)
intcpt = est.intercept_(T=1)
est.fit_cate_intercept = False
np.testing.assert_equal(est.intercept_(T=1), intcpt)
est.refit_final()
with pytest.raises(AttributeError):
est.intercept(T=1)
est.fit(y, T, X=X, W=W, cache_values=False)
with pytest.raises(AssertionError):
est.refit_final()
def test_discrete_treatments(self):
"""
TODO Almost identical to DML test, so consider merging
Test that we can use discrete treatments
"""
dml = LinearDRLearner(model_regression=LinearRegression(),
model_propensity=LogisticRegression(
C=1000, solver='lbfgs', multi_class='auto'),
featurizer=FunctionTransformer(validate=True))
# create a simple artificial setup where effect of moving from treatment
# 1 -> 2 is 2,
# 1 -> 3 is 1, and
# 2 -> 3 is -1 (necessarily, by composing the previous two effects)
# Using an uneven number of examples from different classes,
# and having the treatments in non-lexicographic order,
# Should rule out some basic issues.
dml.fit(np.array([2, 3, 1, 3, 2, 1, 1, 1]), np.array(
[3, 2, 1, 2, 3, 1, 1, 1]), np.ones((8, 1)))
np.testing.assert_almost_equal(dml.effect(np.ones((9, 1)),
T0=np.array(
[1, 1, 1, 2, 2, 2, 3, 3, 3]),
T1=np.array([1, 2, 3, 1, 2, 3, 1, 2, 3])),
[0, 2, 1, -2, 0, -1, -1, 1, 0])
dml.score(np.array([2, 3, 1, 3, 2, 1, 1, 1]), np.array(
[3, 2, 1, 2, 3, 1, 1, 1]), np.ones((8, 1)))
def test_can_summarize(self):
LinearDMLCateEstimator().fit(TestInference.Y,
TestInference.T,
TestInference.X,
TestInference.W,
inference='statsmodels').summary()
LinearDRLearner(fit_cate_intercept=False).fit(
TestInference.Y,
TestInference.T > 0,
TestInference.X,
TestInference.W,
inference=BootstrapInference(5)).summary(1)
def test_can_summarize(self):
LinearDML(model_t=LinearRegression(),
model_y=LinearRegression()).fit(TestInference.Y,
TestInference.T,
TestInference.X,
TestInference.W).summary()
LinearDRLearner(model_regression=LinearRegression(),
model_propensity=LogisticRegression(),
fit_cate_intercept=False).fit(
TestInference.Y,
TestInference.T > 0,
TestInference.X,
TestInference.W,
inference=BootstrapInference(5)).summary(1)
def test_dr_random_state(self):
Y, T, X, W, X_test = self._make_data(500, 2)
for est in [
DRLearner(model_final=RandomForestRegressor(
max_depth=3,
n_estimators=10,
min_samples_leaf=100,
bootstrap=True,
random_state=123),
cv=2,
random_state=123),
LinearDRLearner(random_state=123),
SparseLinearDRLearner(cv=2, random_state=123),
ForestDRLearner(
model_regression=RandomForestRegressor(n_estimators=10,
max_depth=4,
random_state=123),
model_propensity=RandomForestClassifier(n_estimators=10,
max_depth=4,
random_state=123),
cv=2,
random_state=123)
]:
TestRandomState._test_random_state(est, X_test, Y, T, X=X, W=W)
def test_cate_api(self):
"""Test that we correctly implement the CATE API."""
n = 20
def make_random(is_discrete, d):
if d is None:
return None
sz = (n, d) if d > 0 else (n,)
if is_discrete:
while True:
arr = np.random.choice(['a', 'b', 'c'], size=sz)
# ensure that we've got at least two of every element
_, counts = np.unique(arr, return_counts=True)
if len(counts) == 3 and counts.min() > 1:
return arr
else:
return np.random.normal(size=sz)
for d_y in [0, 1]:
is_discrete = True
for d_t in [0, 1]:
for d_x in [2, None]:
for d_w in [2, None]:
W, X, Y, T = [make_random(is_discrete, d)
for is_discrete, d in [(False, d_w),
(False, d_x),
(False, d_y),
(is_discrete, d_t)]]
if (X is None) and (W is None):
continue
d_t_final = 2 if is_discrete else d_t
effect_shape = (n,) + ((d_y,) if d_y > 0 else ())
effect_summaryframe_shape = (
n * (d_y if d_y > 0 else 1), 6)
marginal_effect_shape = ((n,) +
((d_y,) if d_y > 0 else ()) +
((d_t_final,) if d_t_final > 0 else ()))
marginal_effect_summaryframe_shape = (n * (d_y if d_y > 0 else 1),
6 * (d_t_final if d_t_final > 0 else 1))
# since T isn't passed to const_marginal_effect, defaults to one row if X is None
const_marginal_effect_shape = ((n if d_x else 1,) +
((d_y,) if d_y > 0 else ()) +
((d_t_final,) if d_t_final > 0 else()))
const_marginal_effect_summaryframe_shape = (
(n if d_x else 1) * (d_y if d_y > 0 else 1),
6 * (d_t_final if d_t_final > 0 else 1))
for est in [LinearDRLearner(model_propensity=LogisticRegression(C=1000, solver='lbfgs',
multi_class='auto')),
DRLearner(model_propensity=LogisticRegression(multi_class='auto'),
model_regression=LinearRegression(),
model_final=StatsModelsLinearRegression(),
multitask_model_final=True)]:
# TODO: add stratification to bootstrap so that we can use it even with discrete treatments
infs = [None]
if isinstance(est, LinearDRLearner):
infs.append('statsmodels')
for inf in infs:
with self.subTest(d_w=d_w, d_x=d_x, d_y=d_y, d_t=d_t,
is_discrete=is_discrete, est=est, inf=inf):
est.fit(Y, T, X, W, inference=inf)
# make sure we can call the marginal_effect and effect methods
const_marg_eff = est.const_marginal_effect(
X)
marg_eff = est.marginal_effect(T, X)
self.assertEqual(
shape(marg_eff), marginal_effect_shape)
self.assertEqual(
shape(const_marg_eff), const_marginal_effect_shape)
np.testing.assert_array_equal(
marg_eff if d_x else marg_eff[0:1], const_marg_eff)
T0 = np.full_like(T, 'a')
eff = est.effect(X, T0=T0, T1=T)
self.assertEqual(shape(eff), effect_shape)
if inf is not None:
const_marg_eff_int = est.const_marginal_effect_interval(
X)
marg_eff_int = est.marginal_effect_interval(
T, X)
const_marg_effect_inf = est.const_marginal_effect_inference(
X)
T1 = np.full_like(T, 'b')
effect_inf = est.effect_inference(
X, T0=T0, T1=T1)
marg_effect_inf = est.marginal_effect_inference(
T, X)
self.assertEqual(shape(marg_eff_int),
(2,) + marginal_effect_shape)
self.assertEqual(shape(const_marg_eff_int),
(2,) + const_marginal_effect_shape)
self.assertEqual(shape(est.effect_interval(X, T0=T0, T1=T)),
(2,) + effect_shape)
# test const marginal inference
self.assertEqual(shape(const_marg_effect_inf.summary_frame()),
const_marginal_effect_summaryframe_shape)
self.assertEqual(shape(const_marg_effect_inf.point_estimate),
const_marginal_effect_shape)
self.assertEqual(shape(const_marg_effect_inf.stderr),
const_marginal_effect_shape)
self.assertEqual(shape(const_marg_effect_inf.var),
const_marginal_effect_shape)
self.assertEqual(shape(const_marg_effect_inf.pvalue()),
const_marginal_effect_shape)
self.assertEqual(shape(const_marg_effect_inf.zstat()),
const_marginal_effect_shape)
self.assertEqual(shape(const_marg_effect_inf.conf_int()),
(2,) + const_marginal_effect_shape)
np.testing.assert_array_almost_equal(const_marg_effect_inf.conf_int()
[0], const_marg_eff_int[0], decimal=5)
const_marg_effect_inf.population_summary()._repr_html_()
# test effect inference
self.assertEqual(shape(effect_inf.summary_frame()),
effect_summaryframe_shape)
self.assertEqual(shape(effect_inf.point_estimate),
effect_shape)
self.assertEqual(shape(effect_inf.stderr),
effect_shape)
self.assertEqual(shape(effect_inf.var),
effect_shape)
self.assertEqual(shape(effect_inf.pvalue()),
effect_shape)
self.assertEqual(shape(effect_inf.zstat()),
effect_shape)
self.assertEqual(shape(effect_inf.conf_int()),
(2,) + effect_shape)
np.testing.assert_array_almost_equal(effect_inf.conf_int()
[0], est.effect_interval(
X, T0=T0, T1=T1)
[0], decimal=5)
effect_inf.population_summary()._repr_html_()
# test marginal effect inference
self.assertEqual(shape(marg_effect_inf.summary_frame()),
marginal_effect_summaryframe_shape)
self.assertEqual(shape(marg_effect_inf.point_estimate),
marginal_effect_shape)
self.assertEqual(shape(marg_effect_inf.stderr),
marginal_effect_shape)
self.assertEqual(shape(marg_effect_inf.var),
marginal_effect_shape)
self.assertEqual(shape(marg_effect_inf.pvalue()),
marginal_effect_shape)
self.assertEqual(shape(marg_effect_inf.zstat()),
marginal_effect_shape)
self.assertEqual(shape(marg_effect_inf.conf_int()),
(2,) + marginal_effect_shape)
np.testing.assert_array_almost_equal(marg_effect_inf.conf_int()
[0], marg_eff_int[0], decimal=5)
marg_effect_inf.population_summary()._repr_html_()
est.score(Y, T, X, W)
# make sure we can call effect with implied scalar treatments, no matter the
# dimensions of T, and also that we warn when there are multiple treatments
if d_t > 1:
cm = self.assertWarns(Warning)
else:
cm = ExitStack() # ExitStack can be used as a "do nothing" ContextManager
with cm:
effect_shape2 = (
n if d_x else 1,) + ((d_y,) if d_y > 0 else())
eff = est.effect(X, T0='a', T1='b')
self.assertEqual(
shape(eff), effect_shape2)
def test_can_custom_splitter(self):
"""
TODO Almost identical to DML test, so consider merging
"""
# test that we can fit with a KFold instance
dml = LinearDRLearner(model_regression=LinearRegression(),
model_propensity=LogisticRegression(
C=1000, solver='lbfgs', multi_class='auto'),
n_splits=KFold(n_splits=3))
dml.fit(np.array([1, 2, 3, 1, 2, 3]), np.array(
[1, 2, 3, 1, 2, 3]), np.ones((6, 1)))
dml.score(np.array([1, 2, 3, 1, 2, 3]), np.array(
[1, 2, 3, 1, 2, 3]), np.ones((6, 1)))
# test that we can fit with a train/test iterable
dml = LinearDRLearner(model_regression=LinearRegression(),
model_propensity=LogisticRegression(
C=1000, solver='lbfgs', multi_class='auto'),
n_splits=[([0, 1, 2], [3, 4, 5])])
dml.fit(np.array([1, 2, 3, 1, 2, 3]), np.array(
[1, 2, 3, 1, 2, 3]), np.ones((6, 1)))
dml.score(np.array([1, 2, 3, 1, 2, 3]), np.array(
[1, 2, 3, 1, 2, 3]), np.ones((6, 1)))
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)
def test_linear_drlearner_all_attributes(self):
from sklearn.ensemble import GradientBoostingClassifier, GradientBoostingRegressor, RandomForestRegressor
from sklearn.linear_model import LinearRegression, LogisticRegression
from econml.utilities import StatsModelsLinearRegression
import scipy.special
np.random.seed(123)
controls = np.random.uniform(-1, 1, size=(5000, 3))
T = np.random.binomial(2, scipy.special.expit(controls[:, 0]))
sigma = 0.01
y = (1 + .5 * controls[:, 0]) * T + controls[:, 0] + np.random.normal(
0, sigma, size=(5000, ))
for X in [None, controls]:
for W in [None, controls]:
for sample_weight, sample_var in [(None, None),
(np.ones(T.shape[0]),
np.zeros(T.shape[0]))]:
for featurizer in [
None,
PolynomialFeatures(degree=2, include_bias=False)
]:
for models in [(GradientBoostingClassifier(),
GradientBoostingRegressor()),
(LogisticRegression(solver='lbfgs',
multi_class='auto'),
LinearRegression())]:
for inference in [
'statsmodels',
StatsModelsInferenceDiscrete(
cov_type='nonrobust')
]:
with self.subTest(X=X,
W=W,
sample_weight=sample_weight,
sample_var=sample_var,
featurizer=featurizer,
models=models,
inference=inference):
est = LinearDRLearner(
model_propensity=models[0],
model_regression=models[1],
featurizer=featurizer)
if (X is None) and (W is None):
with pytest.raises(
AttributeError) as e_info:
est.fit(
y,
T,
X=X,
W=W,
sample_weight=sample_weight,
sample_var=sample_var)
continue
est.fit(y,
T,
X=X,
W=W,
sample_weight=sample_weight,
sample_var=sample_var,
inference=inference)
if X is not None:
lower, upper = est.effect_interval(
X[:3], T0=0, T1=1)
point = est.effect(X[:3], T0=0, T1=1)
truth = 1 + .5 * X[:3, 0]
TestDRLearner._check_with_interval(
truth, point, lower, upper)
lower, upper = est.const_marginal_effect_interval(
X[:3])
point = est.const_marginal_effect(
X[:3])
truth = np.hstack([
1 + .5 * X[:3, [0]],
2 * (1 + .5 * X[:3, [0]])
])
TestDRLearner._check_with_interval(
truth, point, lower, upper)
else:
lower, upper = est.effect_interval(
T0=0, T1=1)
point = est.effect(T0=0, T1=1)
truth = np.array([1])
TestDRLearner._check_with_interval(
truth, point, lower, upper)
lower, upper = est.const_marginal_effect_interval(
)
point = est.const_marginal_effect()
truth = np.array([[1, 2]])
TestDRLearner._check_with_interval(
truth, point, lower, upper)
for t in [1, 2]:
if X is not None:
lower, upper = est.marginal_effect_interval(
t, X[:3])
point = est.marginal_effect(
t, X[:3])
truth = np.hstack([
1 + .5 * X[:3, [0]],
2 * (1 + .5 * X[:3, [0]])
])
TestDRLearner._check_with_interval(
truth, point, lower, upper)
else:
lower, upper = est.marginal_effect_interval(
t)
point = est.marginal_effect(t)
truth = np.array([[1, 2]])
TestDRLearner._check_with_interval(
truth, point, lower, upper)
assert isinstance(est.score_, float)
assert isinstance(
est.score(y, T, X=X, W=W), float)
if X is not None:
feat_names = ['A', 'B', 'C']
else:
feat_names = []
out_feat_names = feat_names
if X is not None:
if (featurizer is not None):
out_feat_names = featurizer.fit(
X).get_feature_names(
feat_names)
np.testing.assert_array_equal(
est.featurizer.
n_input_features_, 3)
np.testing.assert_array_equal(
est.cate_feature_names(feat_names),
out_feat_names)
if isinstance(models[0],
GradientBoostingClassifier):
np.testing.assert_array_equal(
np.array([
mdl.feature_importances_ for
mdl in est.models_regression
]).shape, [
2, 2 + len(feat_names) +
(W.shape[1]
if W is not None else 0)
])
np.testing.assert_array_equal(
np.array([
mdl.feature_importances_ for
mdl in est.models_propensity
]).shape, [
2,
len(feat_names) +
(W.shape[1]
if W is not None else 0)
])
else:
np.testing.assert_array_equal(
np.array([
mdl.coef_ for mdl in
est.models_regression
]).shape, [
2, 2 + len(feat_names) +
(W.shape[1]
if W is not None else 0)
])
np.testing.assert_array_equal(
np.array([
mdl.coef_ for mdl in
est.models_propensity
]).shape, [
2, 3,
len(feat_names) +
(W.shape[1]
if W is not None else 0)
])
if X is not None:
for t in [1, 2]:
true_coef = np.zeros(
len(out_feat_names))
true_coef[0] = .5 * t
lower, upper = est.model_cate(
T=t).coef__interval()
point = est.model_cate(T=t).coef_
truth = true_coef
TestDRLearner._check_with_interval(
truth, point, lower, upper)
lower, upper = est.coef__interval(
t)
point = est.coef_(t)
truth = true_coef
TestDRLearner._check_with_interval(
truth, point, lower, upper)
for t in [1, 2]:
lower, upper = est.model_cate(
T=t).intercept__interval()
point = est.model_cate(T=t).intercept_
truth = t
TestDRLearner._check_with_interval(
truth, point, lower, upper)
lower, upper = est.intercept__interval(
t)
point = est.intercept_(t)
truth = t
TestDRLearner._check_with_interval(
truth, point, lower, upper)
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())