# Evaluating and Learning Optimal Dynamic Treatment Regimes under Truncation by Death

arXiv: https://arxiv.org/abs/2510.07501 · HTML: https://mlsanity.com/paper/2510.07501/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 40 references that are in the map.
- First posted on arXiv 8 Oct 2025.

## Builds on (most cited first, 6 of 6)

- [Is Pessimism Provably Efficient for Offline RL?](https://mlsanity.com/paper/2012.15085/) (2020)
- [Semiparametric doubly robust targeted double machine learning: a review](https://mlsanity.com/paper/2203.06469/) (2022)
- [On Estimation of Optimal Treatment Regimes For Maximizing t-Year Survival Probability](https://mlsanity.com/paper/1407.7820/) (2014)
- [Multiply robust estimation of causal effects under principal ignorability](https://mlsanity.com/paper/2012.01615/) (2020)
- [Semiparametric Localized Principal Stratification Analysis with Continuous Strata](https://mlsanity.com/paper/2406.13478/) (2024)
- [Bayesian principal stratification with longitudinal data and truncation by death](https://mlsanity.com/paper/2401.00196/) (2023)

## Built on by (most cited first, 1 of 1)

- [Off-Policy Evaluation for Missingness-Aware Policies in MDPs with Rewards Missing Not at Random](https://mlsanity.com/paper/2606.20206/) (2026)

## Abstract (arXiv)

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.