# Learning Robust Treatment Rules for Censored Data

arXiv: https://arxiv.org/abs/2408.09155 · HTML: https://mlsanity.com/paper/2408.09155/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 75 references that are in the map.
- First posted on arXiv 17 Aug 2024.

## Builds on (most cited first, 25 of 25)

- [Generalized Random Forests](https://mlsanity.com/paper/1610.01271/) (2016)
- [Estimating Dynamic Treatment Regimes in Mobile Health Using V-learning](https://mlsanity.com/paper/1611.03531/) (2016)
- [Offline Multi-Action Policy Learning: Generalization and Optimization](https://mlsanity.com/paper/1810.04778/) (2018)
- [Statistical Inference of the Value Function for Reinforcement Learning in Infinite Horizon Settings](https://mlsanity.com/paper/2001.04515/) (2020)
- [Residual Weighted Learning for Estimating Individualized Treatment Rules](https://mlsanity.com/paper/1508.03179/) (2015)
- [Estimating heterogeneous treatment effects with right-censored data via causal survival forests](https://mlsanity.com/paper/2001.09887/) (2020)
- [Estimating Optimal Infinite Horizon Dynamic Treatment Regimes via pT-Learning](https://mlsanity.com/paper/2110.10719/) (2021)
- [Computing B-Stationary Points of Nonsmooth DC Programs](https://mlsanity.com/paper/1511.01796/) (2015)
- [On Estimation of Optimal Treatment Regimes For Maximizing t-Year Survival Probability](https://mlsanity.com/paper/1407.7820/) (2014)
- [Optimal regimes for algorithm-assisted human decision-making](https://mlsanity.com/paper/2203.03020/) (2022)
- [Individualized Decision-Making Under Partial Identification: Three Perspectives, Two Optimality Results, and One Paradox](https://mlsanity.com/paper/2110.10961/) (2021)
- [Median Optimal Treatment Regimes](https://mlsanity.com/paper/2103.01802/) (2021)
- [A Parsimonious Personalized Dose Finding Model via Dimension Reduction](https://mlsanity.com/paper/1802.06156/) (2018)
- [On Robustness of Individualized Decision Rules](https://mlsanity.com/paper/1903.04367/) (2019)
- [Tree based weighted learning for estimating individualized treatment rules with censored data](https://mlsanity.com/paper/1707.09632/) (2017)
- [Multicategory Angle-based Learning for Estimating Optimal Dynamic Treatment Regimes with Censored Data](https://mlsanity.com/paper/2001.04629/) (2020)
- [Efficient and robust transfer learning of optimal individualized treatment regimes with right-censored survival data](https://mlsanity.com/paper/2301.05491/) (2023)
- [Policy Learning with Distributional Welfare](https://mlsanity.com/paper/2311.15878/) (2023)
- [Targeted Learning Ensembles for Optimal Individualized Treatment Rules with Time-to-Event Outcomes](https://mlsanity.com/paper/1702.04682/) (2017)
- [Consistency of survival tree and forest models: splitting bias and correction](https://mlsanity.com/paper/1707.09631/) (2017)
- [Robust regression for optimal individualized treatment rules](https://mlsanity.com/paper/1604.03648/) (2016)
- [Estimation of Individualized Decision Rules Based on an Optimized Covariate-Dependent Equivalent of Random Outcomes](https://mlsanity.com/paper/1908.10742/) (2019)
- [Stochastic Difference-of-Convex Algorithms for Solving nonconvex optimization problems](https://mlsanity.com/paper/1911.04334/) (2019)
- [Stage-Aware Learning for Dynamic Treatments](https://mlsanity.com/paper/2310.19300/) (2023)
- [Counting Process Based Dimension Reduction Methods for Censored Outcomes](https://mlsanity.com/paper/1704.05046/) (2017)

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

- [Deep Optimal Individualized Treatment Rules for Bivariate Survival Outcomes via Adaptive Prediction-Powered Learning](https://mlsanity.com/paper/2605.29464/) (2026)

## Abstract (arXiv)

There is a fast-growing literature on estimating optimal treatment rules directly by maximizing the expected outcome. In biomedical studies and operations applications, censored survival outcome is frequently observed, in which case the truncated mean survival time and survival probability are of great interest. In this paper, we propose two robust criteria for learning optimal treatment rules with censored survival outcomes; the former one targets an optimal treatment rule maximizing the truncated mean survival time, where the cutoff is specified by a given quantile such as median; the latter one targets an optimal treatment rule maximizing buffered survival probabilities, where the predetermined threshold is adjusted to account for the truncated mean survival time. We develop a sampling-based difference-of-convex algorithm for learning the proposed optimal treatment rules, and provide theoretical justifications for them. In simulation studies, our estimators show improved performance compared to existing methods. We also demonstrate the proposed method using AIDS clinical trial data.