Skip to content

Add: support for predict_proba for estimators that support it #776

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

Open
wants to merge 2 commits into
base: main
Choose a base branch
from
Open

Add: support for predict_proba for estimators that support it #776

Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
48905cb
Add: support for predict_proba for estimators that support it
arnauddhaene May 30, 2023
cc0bcb6
Fix: make sure all evaluated models have predict_proba
arnauddhaene May 30, 2023
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
36 changes: 30 additions & 6 deletions econml/metalearners/_metalearners.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -109,7 +109,7 @@ def fit(self, Y, T, *, X, inference=None):
self.models[ind].fit(X[T == ind], Y[T == ind])

def const_marginal_effect(self, X):
"""Calculate the constant marignal treatment effect on a vector of features for each sample.
"""Calculate the constant marginal treatment effect on a vector of features for each sample.

Parameters
----------
Expand All @@ -127,7 +127,14 @@ def const_marginal_effect(self, X):
X = check_array(X)
taus = []
for ind in range(self._d_t[0]):
taus.append(self.models[ind + 1].predict(X) - self.models[0].predict(X))
if (
hasattr(self.models[ind + 1], 'predict_proba') and
hasattr(self.models[0], 'predict_proba')
):
taus.append(self.models[ind + 1].predict_proba(X)[:, 1] - self.models[0].predict_proba(X)[:, 1])
else:
taus.append(self.models[ind + 1].predict(X) - self.models[0].predict(X))

taus = np.column_stack(taus).reshape((-1,) + self._d_t + self._d_y) # shape as of m*d_t*d_y
if self._d_y:
taus = transpose(taus, (0, 2, 1)) # shape as of m*d_y*d_t
Expand Down Expand Up @@ -242,7 +249,12 @@ def const_marginal_effect(self, X=None):
X = check_array(X)
Xs, Ts = broadcast_unit_treatments(X, self._d_t[0] + 1)
feat_arr = np.concatenate((Xs, Ts), axis=1)
prediction = self.overall_model.predict(feat_arr).reshape((-1, self._d_t[0] + 1,) + self._d_y)

if hasattr(self.overall_model, 'predict_proba'):
prediction = self.overall_model.predict_proba(feat_arr)[:, 1].reshape((-1, self._d_t[0] + 1,) + self._d_y)
else:
prediction = self.overall_model.predict(feat_arr).reshape((-1, self._d_t[0] + 1,) + self._d_y)

if self._d_y:
prediction = transpose(prediction, (0, 2, 1))
taus = (prediction - np.repeat(prediction[:, :, 0], self._d_t[0] + 1).reshape(prediction.shape))[:, :, 1:]
Expand Down Expand Up @@ -393,8 +405,17 @@ def const_marginal_effect(self, X):
taus = []
for ind in range(self._d_t[0]):
propensity_scores = self.propensity_models[ind].predict_proba(X)[:, 1:]
tau_hat = propensity_scores * self.cate_controls_models[ind].predict(X).reshape(m, -1) \
+ (1 - propensity_scores) * self.cate_treated_models[ind].predict(X).reshape(m, -1)

if (
hasattr(self.cate_controls_models[ind], 'predict_proba') and
hasattr(self.cate_treated_models[ind], 'predict_proba')
):
tau_hat = propensity_scores * self.cate_controls_models[ind].predict_proba(X)[:, 1].reshape(m, -1) \
+ (1 - propensity_scores) * self.cate_treated_models[ind].predict_proba(X)[:, 1].reshape(m, -1)
else:
tau_hat = propensity_scores * self.cate_controls_models[ind].predict(X).reshape(m, -1) \
+ (1 - propensity_scores) * self.cate_treated_models[ind].predict(X).reshape(m, -1)

taus.append(tau_hat)
taus = np.column_stack(taus).reshape((-1,) + self._d_t + self._d_y) # shape as of m*d_t*d_y
if self._d_y:
Expand Down Expand Up @@ -549,7 +570,10 @@ def const_marginal_effect(self, X):
X = check_array(X)
taus = []
for model in self.final_models:
taus.append(model.predict(X))
if hasattr(model, 'predict_proba'):
taus.append(model.predict_proba(X)[:, 1])
else:
taus.append(model.predict(X))
taus = np.column_stack(taus).reshape((-1,) + self._d_t + self._d_y) # shape as of m*d_t*d_y
if self._d_y:
taus = transpose(taus, (0, 2, 1)) # shape as of m*d_y*d_t
Expand Down