TREE-BASED REINFORCEMENT LEARNING FOR ESTIMATING OPTIMAL DYNAMIC TREATMENT REGIMES

By Yebin Tao, Lu Wang, D. Almirall. 2018

59 citations recorded by the source corpus.

Dynamic treatment regimes (DTRs) are sequences of treatment decision rules, in which treatment may be adapted over time in response to the changing course of an individual. Motivated by the substance use disorder (SUD) study, we propose a tree-based reinforcement learning (T-RL) method to directly estimate optimal DTRs in a multi-stage multi-treatment setting. At each stage, T-RL builds an unsupervised decision tree that directly handles the problem of optimization with multiple treatment comparisons, through a purity measure constructed with augmented inverse probability weighted estimators. For the multiple stages, the algorithm is implemented recursively using backward induction. By combining semiparametric regression with flexible tree-based learning, T-RL is robust, efficient and easy to interpret for the identification of optimal DTRs, as shown in the simulation studies. With the proposed method, we identify dynamic SUD treatment regimes for adolescents.

Subfields: Epidemiology, Experimental and Cognitive Psychology, Statistics and Probability

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Epidemiology

1. Chao Yu
2. Jiming Liu
3. Shamim Nemati
4. Guoqi Ren

In Experimental and Cognitive Psychology

1. Shamim Nemati
2. Chao Yu
3. Jiming Liu

In Statistics and Probability

1. Chao Yu
2. Jiming Liu
3. Shamim Nemati

Citations in the corpus

• Reinforcement Learning in Healthcare: A Survey (2019, methodology): “[217] proposed a tree-based RL method to directly estimate optimal DTRs and identify dynamic SUD treatment regimes for adolescents.”