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A reinforcement learning framework for dynamic mediation analysis

Ge, Lin, Wang, Jitao, Shi, Chengchun, Wu, Zhenke and Song, Rui (2023) A reinforcement learning framework for dynamic mediation analysis. Proceedings of Machine Learning Research, 202. 11050 - 11097. ISSN 1938-7228

[img] Text (A Reinforcement Learning Framework for Dynamic Mediation Analysis) - Accepted Version
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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 pointexposure 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

Item Type: Article
Official URL:
Additional Information: © 2023 The Author(s)
Divisions: Statistics
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Date Deposited: 17 Nov 2023 10:03
Last Modified: 16 Jun 2024 21:30

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