Ambiguous Dynamic Treatment Regimes: A Reinforcement Learning Approach
模糊动态治疗方案：强化学习方法AI 解读 ↓
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模糊动态治疗方案：强化学习方法
- 研究问题
- 当未观测混杂随时间变化且受既往行动影响时，如何在因果模型无法唯一确定的观察资料中学习更优的动态治疗方案？
- 方法与思路
- 作者以一组可能的因果模型评价治疗方案，定义模糊动态治疗方案，并关联模糊部分可观测马尔可夫决策过程来开发强化学习方法。
- 材料陈述的贡献
- 摘要称建立了学习方法的弱一致性和渐近正态性结果，并通过移植后新发糖尿病案例与模拟实验评价方法表现。
- 阅读关注点
- 核对候选因果模型集合如何确定、方案优劣的评价准则，以及理论所需条件和对未观测混杂设定的敏感性。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未经核实；摘要未说明模型集合及最优性条件，不能据此认定观察资料唯一识别因果方案。
原文摘要
A main research goal in various studies is to use an observational data set and provide a new set of counterfactual guidelines that can yield causal improvements. Dynamic Treatment Regimes (DTRs) are widely studied to formalize this process. However, available methods in finding optimal DTRs often rely on assumptions that are violated in real-world applications (e.g., medical decision-making or public policy), especially when (a) the existence of unobserved confounders cannot be ignored, and (b) the unobserved confounders are time-varying (e.g., affected by previous actions). When such assumptions are violated, one often faces ambiguity regarding the underlying causal model. This ambiguity is inevitable, since the dynamics of unobserved confounders and their causal impact on the observed part of the data cannot be understood from the observed data. Motivated by a case study of finding superior treatment regimes for patients who underwent transplantation in our partner hospital and faced a medical condition known as New Onset Diabetes After Transplantation (NODAT), we extend DTRs to a new class termed Ambiguous Dynamic Treatment Regimes (ADTRs), in which the causal impact of treatment regimes is evaluated based on a "cloud" of causal models. We then connect ADTRs to Ambiguous Partially Observable Mark Decision Processes (APOMDPs) and develop Reinforcement Learning methods, which enable using the observed data to efficiently learn an optimal treatment regime. We establish theoretical results for these learning methods, including (weak) consistency and asymptotic normality. We further evaluate the performance of these learning methods both in our case study and in simulation experiments.