fix loss calculation; re-run notebooks; misc cleanup - #19
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gmgeorg
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Aug 20, 2026
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- Fix OutcomeLoss to compute the exact negative marginal log-likelihood of the state mixture (logsumexp) instead of a weighted sum of per-state NLLs.
- Fix propensity metrics (PropensityScoreBinaryCrossentropy, PropensityScoreAUC) to read the treatment label from the last column of y_true instead of a hardcoded [:, 1:] slice, which broke for outcomes with >1 column (e.g. survival's event_time + event_indicator).
- Fix TreatmentMeanAbsoluteError using the wrong n_outcome_true_cols attribute.
- Wire up the binary_survival dataset module in datasets/init.py.
- Add regression tests for the above.
- Re-run notebooks with the updated loss/metrics.
- Fix OutcomeLoss to compute the exact negative marginal log-likelihood of the state mixture (logsumexp) instead of a weighted sum of per-state NLLs. - Fix propensity metrics (PropensityScoreBinaryCrossentropy, PropensityScoreAUC) to read the treatment label from the last column of y_true instead of a hardcoded [:, 1:] slice, which broke for outcomes with >1 column (e.g. survival's event_time + event_indicator). - Fix TreatmentMeanAbsoluteError using the wrong n_outcome_true_cols attribute. - Wire up the binary_survival dataset module in datasets/__init__.py. - Add regression tests for the above. - Re-run notebooks with the updated loss/metrics. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
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