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Vasilis/dmlcoef #283

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Sep 16, 2020
Merged

Vasilis/dmlcoef #283

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5be81d8
Added coef_ and intercept_ to DMLCateEstimator by inheriting LinearMo…
vasilismsr Sep 10, 2020
a2b04aa
tests for new dml coef_ and intercept_ functionality
vasilismsr Sep 10, 2020
b0be5e9
linting
vasilismsr Sep 10, 2020
c36f84a
Update econml/tests/test_dml.py
vsyrgkanis Sep 10, 2020
45af2fe
Update econml/tests/test_dml.py
vsyrgkanis Sep 10, 2020
3923292
Update econml/tests/test_dml.py
vsyrgkanis Sep 10, 2020
10adb0f
linting
vasilismsr Sep 10, 2020
ce5bd7f
fixed test dml
vasilismsr Sep 12, 2020
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4 changes: 2 additions & 2 deletions econml/dml.py
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Original file line number Diff line number Diff line change
Expand Up @@ -50,7 +50,7 @@
from sklearn.utils import check_random_state
from .cate_estimator import (BaseCateEstimator, LinearCateEstimator,
TreatmentExpansionMixin, StatsModelsCateEstimatorMixin,
DebiasedLassoCateEstimatorMixin)
LinearModelFinalCateEstimatorMixin, DebiasedLassoCateEstimatorMixin)
from .inference import StatsModelsInference, GenericSingleTreatmentModelFinalInference
from ._rlearner import _RLearner
from .sklearn_extensions.model_selection import WeightedStratifiedKFold
Expand Down Expand Up @@ -296,7 +296,7 @@ def cate_feature_names(self, input_feature_names=None):
raise AttributeError("Featurizer does not have a method: get_feature_names!")


class DMLCateEstimator(_BaseDMLCateEstimator):
class DMLCateEstimator(LinearModelFinalCateEstimatorMixin, _BaseDMLCateEstimator):
"""
The base class for parametric Double ML estimators. The estimator is a special
case of an :class:`._RLearner` estimator, which in turn is a special case
Expand Down
24 changes: 13 additions & 11 deletions econml/tests/test_dml.py
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Original file line number Diff line number Diff line change
Expand Up @@ -110,7 +110,16 @@ def make_random(n, is_discrete, d):
all_infs.append(BootstrapInference(1))

for est, multi, infs in\
[(LinearDMLCateEstimator(model_y=Lasso(),
[(DMLCateEstimator(model_y=Lasso(),
model_t=model_t,
model_final=Lasso(alpha=0.1, fit_intercept=False),
featurizer=featurizer,
fit_cate_intercept=fit_cate_intercept,
discrete_treatment=is_discrete),
True,
[None] +
([BootstrapInference(n_bootstrap_samples=20)] if not is_discrete else [])),
(LinearDMLCateEstimator(model_y=Lasso(),
model_t='auto',
featurizer=featurizer,
fit_cate_intercept=fit_cate_intercept,
Expand Down Expand Up @@ -171,8 +180,7 @@ def make_random(n, is_discrete, d):
eff = est.effect(X, T0=T0, T1=T)
self.assertEqual(shape(eff), effect_shape)

if isinstance(est, LinearDMLCateEstimator) or\
isinstance(est, SparseLinearDMLCateEstimator):
if not isinstance(est, KernelDMLCateEstimator):
self.assertEqual(shape(est.coef_), coef_shape)
if fit_cate_intercept:
self.assertEqual(shape(est.intercept_), intercept_shape)
Expand All @@ -189,10 +197,7 @@ def make_random(n, is_discrete, d):
(2,) + const_marginal_effect_shape)
self.assertEqual(shape(est.effect_interval(X, T0=T0, T1=T)),
(2,) + effect_shape)
if (isinstance(est,
LinearDMLCateEstimator) or
isinstance(est,
SparseLinearDMLCateEstimator)):
if not isinstance(est, KernelDMLCateEstimator):
self.assertEqual(shape(est.coef__interval()),
(2,) + coef_shape)
if fit_cate_intercept:
Expand Down Expand Up @@ -267,10 +272,7 @@ def make_random(n, is_discrete, d):
marg_effect_inf.population_summary()._repr_html_()

# test coef__inference and intercept__inference
if (isinstance(est,
LinearDMLCateEstimator) or
isinstance(est,
SparseLinearDMLCateEstimator)):
if not isinstance(est, KernelDMLCateEstimator):
if X is None:
cm = pytest.raises(AttributeError)
else:
Expand Down