Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

Refactor DynamicDML to remove incompatible method signatures #717

Merged
merged 1 commit into from
Jan 17, 2023
Merged

Refactor DynamicDML to remove incompatible method signatures #717

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
17 changes: 7 additions & 10 deletions econml/panel/dml/_dml.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -547,32 +547,29 @@ def _gen_model_t(self):
def _gen_model_final(self):
return StatsModelsLinearRegression(fit_intercept=False)

def _gen_ortho_learner_model_nuisance(self, n_periods):
def _gen_ortho_learner_model_nuisance(self):
return _DynamicModelNuisance(
model_t=self._gen_model_t(),
model_y=self._gen_model_y(),
n_periods=n_periods)
n_periods=self._n_periods)

def _gen_ortho_learner_model_final(self, n_periods):
def _gen_ortho_learner_model_final(self):
wrapped_final_model = _DynamicFinalWrapper(
StatsModelsLinearRegression(fit_intercept=False),
fit_cate_intercept=self.fit_cate_intercept,
featurizer=self.featurizer,
use_weight_trick=False)
return _LinearDynamicModelFinal(wrapped_final_model, n_periods=n_periods)
return _LinearDynamicModelFinal(wrapped_final_model, n_periods=self._n_periods)

def _prefit(self, Y, T, *args, groups=None, only_final=False, **kwargs):
# we need to set the number of periods before calling super()._prefit, since that will generate the
# final and nuisance models, which need to have self._n_periods set
u_periods = np.unique(np.unique(groups, return_counts=True)[1])
if len(u_periods) > 1:
raise AttributeError(
"Imbalanced panel. Method currently expects only panels with equal number of periods. Pad your data")
self._n_periods = u_periods[0]
# generate an instance of the final model
self._ortho_learner_model_final = self._gen_ortho_learner_model_final(self._n_periods)
if not only_final:
# generate an instance of the nuisance model
self._ortho_learner_model_nuisance = self._gen_ortho_learner_model_nuisance(self._n_periods)
TreatmentExpansionMixin._prefit(self, Y, T, *args, **kwargs)
super()._prefit(Y, T, *args, **kwargs)

def _postfit(self, Y, T, *args, **kwargs):
super()._postfit(Y, T, *args, **kwargs)
Expand Down