class CausalForestEstimator(BaseITEEstimator):
def fit(self, **kwargs):
if self.model is None:
self.model = CausalForestDML()
self.model.fit(Y=kwargs["y_training"],
T=kwargs["t_training"],
X=kwargs["X_training"])
def predict(self, **kwargs):
preds = self.model.effect_inference(kwargs["X"])
if "return_mean" in kwargs:
out = preds.pred
else:
out = (preds.pred, preds.var)
return out
def test_dowhy(self):
def reg():
return LinearRegression()
def clf():
return LogisticRegression()
Y, T, X, W, Z = self._get_data()
# test at least one estimator from each category
models = {"dml": LinearDML(model_y=reg(), model_t=clf(), discrete_treatment=True,
linear_first_stages=False),
"dr": DRLearner(model_propensity=clf(), model_regression=reg(),
model_final=reg()),
"forestdr": ForestDRLearner(model_propensity=clf(), model_regression=reg()),
"xlearner": XLearner(models=reg(), cate_models=reg(), propensity_model=clf()),
"cfdml": CausalForestDML(model_y=reg(), model_t=clf(), discrete_treatment=True),
"orf": DROrthoForest(n_trees=10, propensity_model=clf(), model_Y=reg()),
"orthoiv": OrthoIV(model_y_xw=reg(),
model_t_xw=clf(),
model_z_xw=reg(),
discrete_treatment=True,
discrete_instrument=False),
"dmliv": DMLIV(fit_cate_intercept=True,
discrete_treatment=True,
discrete_instrument=False),
"driv": LinearDRIV(flexible_model_effect=StatsModelsLinearRegression(fit_intercept=False),
fit_cate_intercept=True,
discrete_instrument=False,
discrete_treatment=True)}
for name, model in models.items():
with self.subTest(name=name):
est = model
if name == "xlearner":
est_dowhy = est.dowhy.fit(Y, T, X=np.hstack((X, W)), W=None)
elif name in ["orthoiv", "dmliv", "driv"]:
est_dowhy = est.dowhy.fit(Y, T, Z=Z, X=X, W=W)
else:
est_dowhy = est.dowhy.fit(Y, T, X=X, W=W)
# test causal graph
est_dowhy.view_model()
# test refutation estimate
est_dowhy.refute_estimate(method_name="random_common_cause")
if name != "orf":
est_dowhy.refute_estimate(method_name="add_unobserved_common_cause",
confounders_effect_on_treatment="binary_flip",
confounders_effect_on_outcome="linear",
effect_strength_on_treatment=0.1,
effect_strength_on_outcome=0.1,)
est_dowhy.refute_estimate(method_name="placebo_treatment_refuter", placebo_type="permute",
num_simulations=3)
est_dowhy.refute_estimate(method_name="data_subset_refuter", subset_fraction=0.8,
num_simulations=3)
def test_dml_random_state(self):
Y, T, X, W, X_test = TestRandomState._make_data(500, 2)
for est in [
NonParamDML(model_y=RandomForestRegressor(n_estimators=10,
max_depth=4,
random_state=123),
model_t=RandomForestClassifier(n_estimators=10,
max_depth=4,
random_state=123),
model_final=RandomForestRegressor(
max_depth=3,
n_estimators=10,
min_samples_leaf=100,
bootstrap=True,
random_state=123),
discrete_treatment=True,
n_splits=2,
random_state=123),
CausalForestDML(
model_y=RandomForestRegressor(n_estimators=10,
max_depth=4,
random_state=123),
model_t=RandomForestClassifier(n_estimators=10,
max_depth=4,
random_state=123),
n_estimators=8,
discrete_treatment=True,
cv=2,
random_state=123),
LinearDML(model_y=RandomForestRegressor(n_estimators=10,
max_depth=4,
random_state=123),
model_t=RandomForestClassifier(n_estimators=10,
max_depth=4,
random_state=123),
discrete_treatment=True,
n_splits=2,
random_state=123),
SparseLinearDML(discrete_treatment=True,
n_splits=2,
random_state=123),
KernelDML(discrete_treatment=True,
n_splits=2,
random_state=123)
]:
TestRandomState._test_random_state(est, X_test, Y, T, X=X, W=W)
def fit(self, **kwargs):
if self.model is None:
self.model = CausalForestDML()
self.model.fit(Y=kwargs["y_training"],
T=kwargs["t_training"],
X=kwargs["X_training"])