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Enabling summary() even when inference not available #363

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merged 6 commits into from
Jan 13, 2021
Merged

Enabling summary() even when inference not available #363

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c9ccd0a
started adding inference with stderr = None
vasilismsr Jan 4, 2021
e618e63
fixed merge
vasilismsr Jan 4, 2021
2a8aacc
fixed merge
vasilismsr Jan 4, 2021
77677a3
enabled auto inference at the DRLearner level
vasilismsr Jan 5, 2021
092ebff
added warning messaage based on review
vasilismsr Jan 6, 2021
bf9ef4e
Merge branch 'master' into vasilis/summary_fix
vsyrgkanis Jan 13, 2021
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26 changes: 13 additions & 13 deletions econml/drlearner.py
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Original file line number Diff line number Diff line change
Expand Up @@ -400,6 +400,14 @@ def __init__(self, *,
categories=categories,
random_state=random_state)

def _get_inference_options(self):
options = super()._get_inference_options()
if not self.multitask_model_final:
options.update(auto=GenericModelFinalInferenceDiscrete)
else:
options.update(auto=lambda: None)
return options

def _gen_ortho_learner_model_nuisance(self):
if self.model_propensity == 'auto':
model_propensity = LogisticRegressionCV(cv=3, solver='lbfgs', multi_class='auto',
Expand All @@ -426,7 +434,7 @@ def _gen_ortho_learner_model_final(self):
@_deprecate_positional("X and W should be passed by keyword only. In a future release "
"we will disallow passing X and W by position.", ['X', 'W'])
def fit(self, Y, T, X=None, W=None, *, sample_weight=None, sample_var=None, groups=None,
cache_values=False, inference=None):
cache_values=False, inference='auto'):
"""
Estimate the counterfactual model from data, i.e. estimates function :math:`\\theta(\\cdot)`.

Expand Down Expand Up @@ -463,6 +471,10 @@ def fit(self, Y, T, X=None, W=None, *, sample_weight=None, sample_var=None, grou
sample_weight=sample_weight, sample_var=sample_var, groups=groups,
cache_values=cache_values, inference=inference)

def refit_final(self, *, inference='auto'):
return super().refit_final(inference=inference)
refit_final.__doc__ = _OrthoLearner.refit_final.__doc__

def score(self, Y, T, X=None, W=None):
"""
Score the fitted CATE model on a new data set. Generates nuisance parameters
Expand Down Expand Up @@ -851,10 +863,6 @@ def fit(self, Y, T, X=None, W=None, *, sample_weight=None, sample_var=None, grou
sample_weight=sample_weight, sample_var=sample_var, groups=groups,
cache_values=cache_values, inference=inference)

def refit_final(self, *, inference='auto'):
return super().refit_final(inference=inference)
refit_final.__doc__ = _OrthoLearner.refit_final.__doc__

@property
def fit_cate_intercept_(self):
return self.model_final_.fit_intercept
Expand Down Expand Up @@ -1151,10 +1159,6 @@ def fit(self, Y, T, X=None, W=None, *, sample_weight=None, sample_var=None, grou
sample_weight=sample_weight, sample_var=None, groups=groups,
cache_values=cache_values, inference=inference)

def refit_final(self, *, inference='auto'):
return super().refit_final(inference=inference)
refit_final.__doc__ = _OrthoLearner.refit_final.__doc__

@property
def fit_cate_intercept_(self):
return self.model_final_.fit_intercept
Expand Down Expand Up @@ -1465,10 +1469,6 @@ def fit(self, Y, T, X=None, W=None, *, sample_weight=None, sample_var=None, grou
sample_weight=sample_weight, sample_var=None, groups=groups,
cache_values=cache_values, inference=inference)

def refit_final(self, *, inference='auto'):
return super().refit_final(inference=inference)
refit_final.__doc__ = _OrthoLearner.refit_final.__doc__

def multitask_model_cate(self):
# Replacing to remove docstring
super().multitask_model_cate()
Expand Down
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