Support target treatment indicator other than 1 #78
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Pull Request Overview
This PR updates the treatment indicator logic to support target treatment arms other than 1, enabling more flexible treatment effect estimation for multi-arm experiments.
- Fixed treatment indicator logic in local treatment effects computation to correctly identify treatment/control groups
- Added type annotations for better code clarity and IDE support
- Improved vectorization in stratified distribution computation
Reviewed Changes
Copilot reviewed 3 out of 3 changed files in this pull request and generated 1 comment.
| File | Description |
|---|---|
| example/example.ipynb | Consolidated function call parameters onto a single line |
| dte_adj/util.py | Added type annotations and fixed treatment indicator logic using equality comparison |
| dte_adj/stratified.py | Vectorized outcome comparison loop for improved efficiency |
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| ) | ||
| d_c_prediction, d_c_psi, d_c_eta = estimator._compute_cumulative_distribution( | ||
| control_treatment_arm, np.zeros(1), X, Z, 1 - D | ||
| control_treatment_arm, np.zeros(1), X, Z, 1 - (target_treatment_arm == D) |
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The treatment indicator logic is incorrect. For computing control treatment propensity, it should use 1 - (control_treatment_arm == D) instead of 1 - (target_treatment_arm == D) to properly identify control group membership.
Suggested change
| control_treatment_arm, np.zeros(1), X, Z, 1 - (target_treatment_arm == D) | |
| control_treatment_arm, np.zeros(1), X, Z, 1 - (control_treatment_arm == D) |
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no, current logic should be correct
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