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Poster
A Reinforcement Learning Framework for Dynamic Mediation Analysis
Lin Ge · Jitao Wang · Chengchun Shi · Zhenke Wu · Rui Song

Tue Jul 25 02:00 PM -- 04:30 PM (PDT) @ Exhibit Hall 1 #429
in Poster Session 1 »
Event URL: https://github.com/linlinlin97/MediationRL »

Mediation analysis learns the causal effect transmitted via mediator variables between treatments and outcomes, and receives increasing attention in various scientific domains to elucidate causal relations. Most existing works focus on point-exposure studies where each subject only receives one treatment at a single time point. However, there are a number of applications (e.g., mobile health) where the treatments are sequentially assigned over time and the dynamic mediation effects are of primary interest. Proposing a reinforcement learning (RL) framework, we are the first to evaluate dynamic mediation effects in settings with infinite horizons. We decompose the average treatment effect into an immediate direct effect, an immediate mediation effect, a delayed direct effect, and a delayed mediation effect. Upon the identification of each effect component, we further develop robust and semi-parametrically efficient estimators under the RL framework to infer these causal effects. The superior performance of the proposed method is demonstrated through extensive numerical studies, theoretical results, and an analysis of a mobile health dataset. A Python implementation of the proposed procedure is available at https://github.com/linlinlin97/MediationRL.

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Author Information

Lin Ge (North Carolina State University)
Jitao Wang (University of Michigan)
Chengchun Shi (London School of Economics and Political Science)
Zhenke Wu (University of Michigan, Ann Arbor)
Zhenke Wu

Zhenke Wu’s research involves the development of statistical methods that inform health decisions made by individuals. He is particularly interested in scalable Bayesian methods that integrate multiple sources of evidence, with a focus on hierarchical latent variable modeling. He also works on sequential decision making by developing new statistical tools for reinforcement learning and micro-randomized trials. He has developed methods to estimate the etiology of childhood pneumonia, cause-of-death distributions using verbal autospy, autoantibody signatures for subsetting autoimmune disease patients, and to estimate time-varying causal effects of mobile prompts upon lagged physical, mental and behavioral health outcomes. Zhenke has developed original methods and software that are now used by investigators from research institutes such as US CDC and Johns Hopkins, as well as site investigators from developing countries, e.g., Kenya, South Africa, Gambia, Mali, Zambia, Thailand and Bangladesh. Zhenke completed a BS in Math at Fudan University in 2009 and a PhD in Biostatistics from the Johns Hopkins University in 2014 and then stayed at Hopkins for his postdoctoral training. Since 2016, Zhenke is Assistant Professor of Biostatistics, and Research Assistant Professor in Michigan Institute for Data Science (MIDAS) at University of Michigan, Ann Arbor. When not thinking about Statistics, you can often find me playing basketball, running, rock climbing, hiking, or downhill skiing.

Rui Song (Amazon Inc)

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