Near-Optimal Reinforcement Learning in Dynamic Treatment Regimes

2019-12-01 · NeurIPS 2019 12 · Junzhe Zhang, Elias Bareinboim

A dynamic treatment regime (DTR) consists of a sequence of decision rules, one per stage of intervention, that dictates how to determine the treatment assignment to patients based on evolving treatments and covariates' history. These regimes are particularly effective for managing chronic disorders and is arguably one of the key aspects towards more personalized decision-making. In this paper, we investigate the online reinforcement learning (RL) problem for selecting optimal DTRs provided that observational data is available. We develop the first adaptive algorithm that achieves near-optimal regret in DTRs in online settings, without any access to historical data. We further derive informative bounds on the system dynamics of the underlying DTR from confounded, observational data. Finally, we combine these results and develop a novel RL algorithm that efficiently learns the optimal DTR while leveraging the abundant, yet imperfect confounded observations.

📄 PDF Abstract BibTeX

Code (0)

등록된 구현이 없습니다.

Tasks

Decision Making reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Deep Reinforcement Learning for Dynamic Treatment Regimes on Medical Registry Data

2018-01-28 · Ning Liu, Ying Liu, Brent Logan, Zhiyuan Xu 외

This paper presents the first deep reinforcement learning (DRL) framework to estimate the optimal Dynamic Treatment Regimes from observational medical data. This framework is more flexible and adaptive for high dimension…

Deep Reinforcement Learning reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Ambiguous Dynamic Treatment Regimes: A Reinforcement Learning Approach

2021-12-08 · Soroush Saghafian

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 fo…

counterfactual Decision Making reinforcement-learning Reinforcement Learning+1

Estimating Dynamic Treatment Regimes in Mobile Health Using V-learning

2016-11-10 · Daniel J. Luckett, Eric B. Laber, Anna R. Kahkoska, David M. Maahs 외

The vision for precision medicine is to use individual patient characteristics to inform a personalized treatment plan that leads to the best healthcare possible for each patient. Mobile technologies have an important ro…

Decision Making Reinforcement Learning

On Multiple Robustness of Proximal Dynamic Treatment Regimes

2025-10-23 · Yuanshan Gao, Yang Bai, Yifan Cui arxiv

Dynamic treatment regimes are sequential decision rules that adapt treatment according to individual time-varying characteristics and outcomes to achieve optimal effects, with applications in precision medicine, personal…

Causal Inference

Optimal Dynamic Treatment Regimes and Partial Welfare Ordering

2019-12-20 · Sukjin Han

Dynamic treatment regimes are treatment allocations tailored to heterogeneous individuals. The optimal dynamic treatment regime is a regime that maximizes counterfactual welfare. We introduce a framework in which we can …

counterfactual