def test_parameter_passing(self):
for gen in [DML, NonParamDML]:
est = gen(model_y=LinearRegression(), model_t=LinearRegression(),
model_final=LinearRegression(),
mc_iters=2, mc_agg='median')
assert est.mc_iters == 2
assert est.mc_agg == 'median'
for gen in [LinearDML, SparseLinearDML, KernelDML, ForestDML]:
est = gen(model_y=LinearRegression(), model_t=LinearRegression(),
mc_iters=2, mc_agg='median')
assert est.mc_iters == 2
assert est.mc_agg == 'median'
for gen in [DRLearner, LinearDRLearner, SparseLinearDRLearner, ForestDRLearner]:
est = gen(mc_iters=2, mc_agg='median')
assert est.mc_iters == 2
assert est.mc_agg == 'median'
for gen in [DMLATEIV(model_Y_W=LinearRegression(),
model_T_W=LinearRegression(),
model_Z_W=LinearRegression(), mc_iters=2, mc_agg='median'),
ProjectedDMLATEIV(model_Y_W=LinearRegression(),
model_T_W=LinearRegression(),
model_T_WZ=LinearRegression(), mc_iters=2, mc_agg='median'),
DMLIV(model_Y_X=LinearRegression(),
model_T_X=LinearRegression(),
model_T_XZ=LinearRegression(),
model_final=LinearRegression(), mc_iters=2, mc_agg='median'),
NonParamDMLIV(model_Y_X=LinearRegression(),
model_T_X=LinearRegression(),
model_T_XZ=LinearRegression(),
model_final=LinearRegression(), mc_iters=2, mc_agg='median'),
IntentToTreatDRIV(model_Y_X=LinearRegression(),
model_T_XZ=LinearRegression(),
flexible_model_effect=LinearRegression(), mc_iters=2, mc_agg='median'),
LinearIntentToTreatDRIV(model_Y_X=LinearRegression(),
model_T_XZ=LinearRegression(),
flexible_model_effect=LinearRegression(),
mc_iters=2, mc_agg='median')]:
assert est.mc_iters == 2
assert est.mc_agg == 'median'
def test_orthoiv_random_state(self):
Y, T, X, W, X_test = self._make_data(500, 2)
for est in [
DMLATEIV(model_Y_W=RandomForestRegressor(n_estimators=10,
max_depth=4,
random_state=123),
model_T_W=RandomForestClassifier(n_estimators=10,
max_depth=4,
random_state=123),
model_Z_W=RandomForestClassifier(n_estimators=10,
max_depth=4,
random_state=123),
discrete_treatment=True,
discrete_instrument=True,
cv=2,
random_state=123),
ProjectedDMLATEIV(
model_Y_W=RandomForestRegressor(n_estimators=10,
max_depth=4,
random_state=123),
model_T_W=RandomForestClassifier(n_estimators=10,
max_depth=4,
random_state=123),
model_T_WZ=RandomForestClassifier(n_estimators=10,
max_depth=4,
random_state=123),
discrete_treatment=True,
discrete_instrument=True,
cv=2,
random_state=123)
]:
TestRandomState._test_random_state(est, None, Y, T, W=W, Z=T)
for est in [
DMLIV(model_Y_X=RandomForestRegressor(n_estimators=10,
max_depth=4,
random_state=123),
model_T_X=RandomForestClassifier(n_estimators=10,
max_depth=4,
random_state=123),
model_T_XZ=RandomForestClassifier(n_estimators=10,
max_depth=4,
random_state=123),
model_final=LinearRegression(fit_intercept=False),
discrete_treatment=True,
discrete_instrument=True,
cv=2,
random_state=123),
NonParamDMLIV(
model_Y_X=RandomForestRegressor(n_estimators=10,
max_depth=4,
random_state=123),
model_T_X=RandomForestClassifier(n_estimators=10,
max_depth=4,
random_state=123),
model_T_XZ=RandomForestClassifier(n_estimators=10,
max_depth=4,
random_state=123),
model_final=LinearRegression(),
discrete_treatment=True,
discrete_instrument=True,
cv=2,
random_state=123)
]:
TestRandomState._test_random_state(est, X_test, Y, T, X=X, Z=T)
for est in [
IntentToTreatDRIV(
model_Y_X=RandomForestRegressor(n_estimators=10,
max_depth=4,
random_state=123),
model_T_XZ=RandomForestClassifier(n_estimators=10,
max_depth=4,
random_state=123),
flexible_model_effect=RandomForestRegressor(
n_estimators=10, max_depth=4, random_state=123),
cv=2,
random_state=123),
LinearIntentToTreatDRIV(
model_Y_X=RandomForestRegressor(n_estimators=10,
max_depth=4,
random_state=123),
model_T_XZ=RandomForestClassifier(n_estimators=10,
max_depth=4,
random_state=123),
flexible_model_effect=RandomForestRegressor(
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,
Z=T)
def test_orthoiv(self):
y, T, X, W = self._get_data()
Z = T.copy()
est = DMLATEIV(model_Y_W=LinearRegression(),
model_T_W=LinearRegression(),
model_Z_W=LinearRegression(),
mc_iters=2)
est.fit(y, T, W=W, Z=Z, cache_values=True)
est.refit_final()
est.model_Y_W = Lasso()
est.model_T_W = ElasticNet()
est.model_Z_W = WeightedLasso()
est.fit(y, T, W=W, Z=Z, cache_values=True)
assert isinstance(est.models_nuisance_[0]._model_Y_W._model, Lasso)
assert isinstance(est.models_nuisance_[0]._model_T_W._model, ElasticNet)
assert isinstance(est.models_nuisance_[0]._model_Z_W._model, WeightedLasso)
est = ProjectedDMLATEIV(model_Y_W=LinearRegression(),
model_T_W=LinearRegression(),
model_T_WZ=LinearRegression(),
mc_iters=2)
est.fit(y, T, W=W, Z=Z, cache_values=True)
est.refit_final()
est.model_Y_W = Lasso()
est.model_T_W = ElasticNet()
est.model_T_WZ = WeightedLasso()
est.fit(y, T, W=W, Z=Z, cache_values=True)
assert isinstance(est.models_nuisance_[0]._model_Y_W._model, Lasso)
assert isinstance(est.models_nuisance_[0]._model_T_W._model, ElasticNet)
assert isinstance(est.models_nuisance_[0]._model_T_WZ._model, WeightedLasso)
est = DMLIV(model_Y_X=LinearRegression(),
model_T_X=LinearRegression(),
model_T_XZ=LinearRegression(),
model_final=LinearRegression(fit_intercept=False),
mc_iters=2)
est.fit(y, T, X=X, Z=Z, cache_values=True)
np.testing.assert_equal(len(est.coef_), X.shape[1])
est.featurizer = PolynomialFeatures(degree=2, include_bias=False)
est.refit_final()
np.testing.assert_equal(len(est.coef_), X.shape[1]**2)
est.intercept_
est.fit_cate_intercept = False
est.intercept_
est.refit_final()
with pytest.raises(AttributeError):
est.intercept_
est.model_Y_X = Lasso()
est.model_T_X = ElasticNet()
est.model_T_XZ = WeightedLasso()
est.fit(y, T, X=X, Z=Z, cache_values=True)
assert isinstance(est.models_Y_X[0], Lasso)
assert isinstance(est.models_T_X[0], ElasticNet)
assert isinstance(est.models_T_XZ[0], WeightedLasso)
est = DMLIV(model_Y_X=LinearRegression(),
model_T_X=LinearRegression(),
model_T_XZ=LinearRegression(),
model_final=LinearRegression(fit_intercept=False),
mc_iters=2)
est.fit(y, T, X=X, Z=Z, cache_values=True)
np.testing.assert_equal(len(est.coef_), X.shape[1])
est.featurizer = PolynomialFeatures(degree=2, include_bias=False)
est.refit_final()
np.testing.assert_equal(len(est.coef_), X.shape[1]**2)
est.intercept_
est.fit_cate_intercept = False
est.intercept_
est.refit_final()
with pytest.raises(AttributeError):
est.intercept_
est.model_Y_X = Lasso()
est.model_T_X = ElasticNet()
est.model_T_XZ = WeightedLasso()
est.fit(y, T, X=X, Z=Z, cache_values=True)
assert isinstance(est.models_nuisance_[0]._model_Y_X._model, Lasso)
assert isinstance(est.models_nuisance_[0]._model_T_X._model, ElasticNet)
assert isinstance(est.models_nuisance_[0]._model_T_XZ._model, WeightedLasso)
est = NonParamDMLIV(model_Y_X=LinearRegression(),
model_T_X=LinearRegression(),
model_T_XZ=LinearRegression(),
model_final=LinearRegression(fit_intercept=True),
mc_iters=2)
est.fit(y, T, X=X, Z=Z, cache_values=True)
est.featurizer = PolynomialFeatures(degree=2, include_bias=False)
est.model_final = WeightedLasso()
est.refit_final()
assert isinstance(est.model_cate, WeightedLasso)
assert isinstance(est.featurizer_, PolynomialFeatures)
est = IntentToTreatDRIV(model_Y_X=LinearRegression(), model_T_XZ=LogisticRegression(),
flexible_model_effect=LinearRegression())
est.fit(y, T, X=X, W=W, Z=Z, cache_values=True)
assert est.model_final is None
assert isinstance(est.model_final_, LinearRegression)
est.flexible_model_effect = Lasso()
est.refit_final()
assert est.model_final is None
assert isinstance(est.model_final_, Lasso)
est.model_final = Lasso()
est.refit_final()
assert isinstance(est.model_final, Lasso)
assert isinstance(est.model_final_, Lasso)
assert isinstance(est.models_nuisance_[0]._prel_model_effect.model_final_, LinearRegression)
est.fit(y, T, X=X, W=W, Z=Z, cache_values=True)
assert isinstance(est.models_nuisance_[0]._prel_model_effect.model_final_, Lasso)
est = LinearIntentToTreatDRIV(model_Y_X=LinearRegression(), model_T_XZ=LogisticRegression(),
flexible_model_effect=LinearRegression())
est.fit(y, T, X=X, W=W, Z=Z, cache_values=True)
est.fit_cate_intercept = False
est.intercept_
est.intercept__interval()
est.refit_final()
with pytest.raises(AttributeError):
est.intercept_
with pytest.raises(AttributeError):
est.intercept__interval()
with pytest.raises(ValueError):
est.model_final = LinearRegression()
est.flexible_model_effect = Lasso()
est.fit(y, T, X=X, W=W, Z=Z, cache_values=True)
assert isinstance(est.models_nuisance_[0]._prel_model_effect.model_final_, Lasso)
def test_bad_splits_discrete(self):
"""
Tests that when some training splits in a crossfit fold don't contain all treatments then an error
is raised.
"""
Y = np.array([2, 3, 1, 3, 2, 1, 1, 1])
bad = np.array([2, 2, 1, 2, 1, 1, 1, 1])
W = np.ones((8, 1))
ok = np.array([1, 2, 3, 1, 2, 3, 1, 2])
models = [Lasso(), Lasso(), Lasso()]
est = DMLATEIV(*models, n_splits=[(np.arange(4, 8), np.arange(4))])
est.fit(Y, T=bad, Z=bad,
W=W) # imbalance ok with continuous instrument/treatment
models = [Lasso(), LogisticRegression(), Lasso()]
est = DMLATEIV(*models,
n_splits=[(np.arange(4, 8), np.arange(4))],
discrete_treatment=True)
with pytest.raises(AttributeError):
est.fit(Y, T=bad, Z=ok, W=W)
models = [Lasso(), Lasso(), LogisticRegression()]
est = DMLATEIV(*models,
n_splits=[(np.arange(4, 8), np.arange(4))],
discrete_instrument=True)
with pytest.raises(AttributeError):
est.fit(Y, T=ok, Z=bad, W=W)
def test_cate_api(self):
"""Test that we correctly implement the CATE API."""
n = 30
def size(n, d):
return (n, d) if d >= 0 else (n, )
def make_random(is_discrete, d):
if d is None:
return None
sz = size(n, d)
if is_discrete:
while True:
arr = np.random.choice(['a', 'b', 'c'], size=sz)
# ensure that we've got at least two of every row
_, counts = np.unique(arr, return_counts=True, axis=0)
if len(counts) == 3**(d if d > 0 else
1) and counts.min() > 1:
return arr
else:
return np.random.normal(size=sz)
def eff_shape(n, d_y):
return (n, ) + ((d_y, ) if d_y > 0 else ())
def marg_eff_shape(n, d_y, d_t_final):
return ((n, ) + ((d_y, ) if d_y > 0 else
()) + ((d_t_final, ) if d_t_final > 0 else ()))
# since T isn't passed to const_marginal_effect, defaults to one row if X is None
def const_marg_eff_shape(n, d_x, d_y, d_t_final):
return ((n if d_x else 1, ) + ((d_y, ) if d_y > 0 else ()) +
((d_t_final, ) if d_t_final > 0 else ()))
for d_t in [2, 1, -1]:
n_t = d_t if d_t > 0 else 1
for discrete_t in [True, False] if n_t == 1 else [False]:
for d_y in [3, 1, -1]:
for d_q in [2, None]:
for d_z in [2, 1]:
if d_z < n_t:
continue
for discrete_z in [True, False
] if d_z == 1 else [False]:
Z1, Q, Y, T1 = [
make_random(is_discrete, d)
for is_discrete, d in [(
discrete_z,
d_z), (False,
d_q), (False,
d_y), (discrete_t, d_t)]
]
if discrete_t and discrete_z:
# need to make sure we get all *joint* combinations
arr = make_random(True, 2)
Z1 = arr[:, 0].reshape(size(n, d_z))
T1 = arr[:, 0].reshape(size(n, d_t))
d_t_final1 = 2 if discrete_t else d_t
if discrete_t:
# IntentToTreat only supports binary treatments/instruments
T2 = T1.copy()
T2[T1 == 'c'] = np.random.choice(
['a', 'b'],
size=np.count_nonzero(T1 == 'c'))
d_t_final2 = 1
if discrete_z:
# IntentToTreat only supports binary treatments/instruments
Z2 = Z1.copy()
Z2[Z1 == 'c'] = np.random.choice(
['a', 'b'],
size=np.count_nonzero(Z1 == 'c'))
effect_shape = eff_shape(n, d_y)
model_t = LogisticRegression(
) if discrete_t else Lasso()
model_z = LogisticRegression(
) if discrete_z else Lasso()
# TODO: add stratification to bootstrap so that we can use it
# even with discrete treatments
all_infs = [None]
if not (discrete_t or discrete_z):
all_infs.append(BootstrapInference(1))
estimators = [
(DMLATEIV(model_Y_W=Lasso(),
model_T_W=model_t,
model_Z_W=model_z,
discrete_treatment=discrete_t,
discrete_instrument=discrete_z),
True, all_infs),
(ProjectedDMLATEIV(
model_Y_W=Lasso(),
model_T_W=model_t,
model_T_WZ=model_t,
discrete_treatment=discrete_t,
discrete_instrument=discrete_z), False,
all_infs),
(DMLIV(model_Y_X=Lasso(),
model_T_X=model_t,
model_T_XZ=model_t,
model_final=Lasso(),
discrete_treatment=discrete_t,
discrete_instrument=discrete_z),
False, all_infs)
]
if d_q and discrete_t and discrete_z:
# IntentToTreat requires X
estimators.append((LinearIntentToTreatDRIV(
model_Y_X=Lasso(),
model_T_XZ=model_t,
flexible_model_effect=WeightedLasso(),
n_splits=2), False, all_infs +
['statsmodels']))
for est, multi, infs in estimators:
if not (
multi
) and d_y > 1 or d_t > 1 or d_z > 1:
continue
# ensure we can serialize unfit estimator
pickle.dumps(est)
d_ws = [None]
if isinstance(est,
LinearIntentToTreatDRIV):
d_ws.append(2)
for d_w in d_ws:
W = make_random(False, d_w)
for inf in infs:
with self.subTest(
d_z=d_z,
d_x=d_q,
d_y=d_y,
d_t=d_t,
discrete_t=discrete_t,
discrete_z=discrete_z,
est=est,
inf=inf):
Z = Z1
T = T1
d_t_final = d_t_final1
X = Q
d_x = d_q
if isinstance(
est,
(DMLATEIV,
ProjectedDMLATEIV)):
# these support only W but not X
W = Q
X = None
d_x = None
def fit():
return est.fit(
Y,
T,
Z=Z,
W=W,
inference=inf)
def score():
return est.score(Y,
T,
Z=Z,
W=W)
else:
# these support only binary, not general discrete T and Z
if discrete_t:
T = T2
d_t_final = d_t_final2
if discrete_z:
Z = Z2
if isinstance(
est,
LinearIntentToTreatDRIV
):
def fit():
return est.fit(
Y,
T,
Z=Z,
X=X,
W=W,
inference=inf)
def score():
return est.score(
Y,
T,
Z=Z,
X=X,
W=W)
else:
def fit():
return est.fit(
Y,
T,
Z=Z,
X=X,
inference=inf)
def score():
return est.score(
Y, T, Z=Z, X=X)
marginal_effect_shape = marg_eff_shape(
n, d_y, d_t_final)
const_marginal_effect_shape = const_marg_eff_shape(
n, d_x, d_y, d_t_final)
fit()
# ensure we can serialize fit estimator
pickle.dumps(est)
# 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'
) if discrete_t else np.zeros_like(
T)
eff = est.effect(X,
T0=T0,
T1=T)
self.assertEqual(
shape(eff), effect_shape)
# TODO: add tests for extra properties like coef_ where they exist
if inf is not None:
const_marg_eff_int = est.const_marginal_effect_interval(
X)
marg_eff_int = est.marginal_effect_interval(
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)
# TODO: add tests for extra properties like coef_ where they exist
score()
# 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:
# ExitStack can be used as a "do nothing" ContextManager
cm = ExitStack()
with cm:
effect_shape2 = (
n if d_x else 1, ) + (
(d_y, )
if d_y > 0 else ())
eff = est.effect(
X
) if not discrete_t else est.effect(
X, T0='a', T1='b')
self.assertEqual(
shape(eff),
effect_shape2)