Evaluating and Learning Optimal Dynamic Treatment Regimes Under Truncation by Death
Abstract
Truncation by death, a prevalent challenge in critical care, renders traditional dynamic treatment regime (DTR) evaluation inapplicable due to ill-defined potential outcomes. We introduce a principal stratification-based method, focusing on the always-survivor value function. We derive a semiparametrically efficient, multiply robust estimator for multi-stage DTRs, demonstrating its robustness and efficiency. Empirical validation and an application to electronic health records showcase its utility for personalized treatment optimization.
Cite
Text
Park et al. "Evaluating and Learning Optimal Dynamic Treatment Regimes Under Truncation by Death." Advances in Neural Information Processing Systems, 2025.Markdown
[Park et al. "Evaluating and Learning Optimal Dynamic Treatment Regimes Under Truncation by Death." Advances in Neural Information Processing Systems, 2025.](https://mlanthology.org/neurips/2025/park2025neurips-evaluating/)BibTeX
@inproceedings{park2025neurips-evaluating,
title = {{Evaluating and Learning Optimal Dynamic Treatment Regimes Under Truncation by Death}},
author = {Park, Sihyung and Lu, Wenbin and Yang, Shu},
booktitle = {Advances in Neural Information Processing Systems},
year = {2025},
url = {https://mlanthology.org/neurips/2025/park2025neurips-evaluating/}
}