Skip to content

Fix: Ensure coef_start is a scalar and convert bracket guesses to floats to ensure scipy compatibility #384

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

Merged
SvenKlaassen merged 1 commit into from
Jan 14, 2026
Merged

Fix: Ensure coef_start is a scalar and convert bracket guesses to floats to ensure scipy compatibility #384

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
6 changes: 5 additions & 1 deletion doubleml/utils/_estimation.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -305,14 +305,18 @@ def _predict_zero_one_propensity(learner, X):


def _get_bracket_guess(score, coef_start, coef_bounds):
# Ensure coef_start is a scalar (handles 1-element arrays from optimizers)
if isinstance(coef_start, np.ndarray):
coef_start = coef_start.item()

max_bracket_length = coef_bounds[1] - coef_bounds[0]
b_guess = coef_bounds
delta = 0.1
s_different = False
while (not s_different) & (delta <= 1.0):
a = np.maximum(coef_start - delta * max_bracket_length / 2, coef_bounds[0])
b = np.minimum(coef_start + delta * max_bracket_length / 2, coef_bounds[1])
b_guess = (a, b)
b_guess = (float(a), float(b))
f_a = score(b_guess[0])
f_b = score(b_guess[1])
s_different = np.sign(f_a) != np.sign(f_b)
Expand Down
Loading