in econml/iv/dr/_dr.py [0:0]
def __init__(self, *,
model_y_xw="auto",
model_t_xw="auto",
model_z_xw="auto",
model_t_xwz="auto",
model_tz_xw="auto",
flexible_model_effect="auto",
prel_cate_approach="driv",
prel_cv=1,
prel_opt_reweighted=True,
projection=False,
featurizer=None,
n_estimators=1000,
max_depth=None,
min_samples_split=5,
min_samples_leaf=5,
min_weight_fraction_leaf=0.,
max_features="auto",
min_impurity_decrease=0.,
max_samples=.45,
min_balancedness_tol=.45,
honest=True,
subforest_size=4,
n_jobs=-1,
verbose=0,
cov_clip=1e-3,
opt_reweighted=False,
discrete_instrument=False,
discrete_treatment=False,
categories='auto',
cv=2,
mc_iters=None,
mc_agg='mean',
random_state=None):
self.n_estimators = n_estimators
self.max_depth = max_depth
self.min_samples_split = min_samples_split
self.min_samples_leaf = min_samples_leaf
self.min_weight_fraction_leaf = min_weight_fraction_leaf
self.max_features = max_features
self.min_impurity_decrease = min_impurity_decrease
self.max_samples = max_samples
self.min_balancedness_tol = min_balancedness_tol
self.honest = honest
self.subforest_size = subforest_size
self.n_jobs = n_jobs
self.verbose = verbose
super().__init__(model_y_xw=model_y_xw,
model_t_xw=model_t_xw,
model_z_xw=model_z_xw,
model_t_xwz=model_t_xwz,
model_tz_xw=model_tz_xw,
flexible_model_effect=flexible_model_effect,
model_final=None,
prel_cate_approach=prel_cate_approach,
prel_cv=prel_cv,
prel_opt_reweighted=prel_opt_reweighted,
projection=projection,
featurizer=featurizer,
fit_cate_intercept=False,
cov_clip=cov_clip,
opt_reweighted=opt_reweighted,
discrete_instrument=discrete_instrument,
discrete_treatment=discrete_treatment,
categories=categories,
cv=cv,
mc_iters=mc_iters,
mc_agg=mc_agg,
random_state=random_state)