def test_with_econml(self):
"""Test that we can bootstrap econml estimators."""
x = np.random.normal(size=(1000, 2))
t = np.random.normal(size=(1000, 1))
t2 = np.random.normal(size=(1000, 1))
y = x[:, 0:1] * 0.5 + t + np.random.normal(size=(1000, 1))
est = LinearDMLCateEstimator(LinearRegression(), LinearRegression())
est.fit(y, t, x)
bs = BootstrapEstimator(est, 50)
# test that we can fit with the same arguments as the base estimator
bs.fit(y, t, x)
# test that we can get the same attribute for the bootstrap as the original, with the same shape
self.assertEqual(np.shape(est.coef_), np.shape(bs.coef_))
# test that we can get an interval for the same attribute for the bootstrap as the original,
# with the same shape for the lower and upper bounds
lower, upper = bs.coef__interval()
for bound in [lower, upper]:
self.assertEqual(np.shape(est.coef_), np.shape(bound))
# test that the lower and upper bounds differ
assert (lower <= upper).all()
assert (lower < upper).any()
# test that we can do the same thing once we provide percentile bounds
lower, upper = bs.coef__interval(lower=10, upper=90)
for bound in [lower, upper]:
self.assertEqual(np.shape(est.coef_), np.shape(bound))
# test that we can do the same thing with the results of a method, rather than an attribute
self.assertEqual(np.shape(est.effect(x, T0=t, T1=t2)),
np.shape(bs.effect(x, T0=t, T1=t2)))
# test that we can get an interval for the same attribute for the bootstrap as the original,
# with the same shape for the lower and upper bounds
lower, upper = bs.effect_interval(x, T0=t, T1=t2)
for bound in [lower, upper]:
self.assertEqual(np.shape(est.effect(x, T0=t, T1=t2)),
np.shape(bound))
# test that the lower and upper bounds differ
assert (lower <= upper).all()
assert (lower < upper).any()
# test that we can do the same thing once we provide percentile bounds
lower, upper = bs.effect_interval(x, T0=t, T1=t2, lower=10, upper=90)
for bound in [lower, upper]:
self.assertEqual(np.shape(est.effect(x, T0=t, T1=t2)),
np.shape(bound))
# test that the lower and upper bounds differ
assert (lower <= upper).all()
assert (lower < upper).any()
def test_can_use_statsmodel_inference(self):
"""Test that we can use statsmodels to generate confidence intervals"""
dml = LinearDMLCateEstimator(LinearRegression(),
LogisticRegression(C=1000),
discrete_treatment=True)
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, 2, 2, 2, 3, 3,
3]),
T1=np.array([1, 2, 3, 1, 2, 3, 1, 2,
3]),
alpha=0.05)
point = 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]))
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(alpha=0.05)
point = dml.coef_
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.intercept__interval(alpha=0.05)
point = dml.intercept_
assert len(interval) == 2
lo, hi = interval
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_discrete_treatments(self):
"""Test that we can use discrete treatments"""
dml = LinearDMLCateEstimator(LinearRegression(),
LogisticRegression(C=1000),
featurizer=FunctionTransformer(),
discrete_treatment=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)), np.array([1, 1, 1, 2, 2, 2, 3, 3, 3]),
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_internal(self):
"""Test that the internal use of bootstrap within an estimator works."""
x = np.random.normal(size=(1000, 2))
t = np.random.normal(size=(1000, 1))
t2 = np.random.normal(size=(1000, 1))
y = x[:, 0:1] * 0.5 + t + np.random.normal(size=(1000, 1))
est = LinearDMLCateEstimator(LinearRegression(), LinearRegression())
est.fit(y, t, x, inference='bootstrap')
# test that we can get an interval for the same attribute for the bootstrap as the original,
# with the same shape for the lower and upper bounds
eff = est.effect(x, T0=t, T1=t2)
lower, upper = est.effect_interval(x, T0=t, T1=t2)
for bound in [lower, upper]:
self.assertEqual(np.shape(eff), np.shape(bound))
# test that the lower and upper bounds differ
assert (lower <= upper).all()
assert (lower < upper).any()
# test that the estimated effect is usually within the bounds
assert np.mean(np.logical_and(lower <= eff, eff <= upper)) >= 0.9
# test that we can do the same thing once we provide alpha explicitly
lower, upper = est.effect_interval(x, T0=t, T1=t2, alpha=0.2)
for bound in [lower, upper]:
self.assertEqual(np.shape(eff), np.shape(bound))
# test that the lower and upper bounds differ
assert (lower <= upper).all()
assert (lower < upper).any()
# test that the estimated effect is usually within the bounds
assert np.mean(np.logical_and(lower <= eff, eff <= upper)) >= 0.8
