ArXiv Preprint
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.
Lin Ge, Jitao Wang, Chengchun Shi, Zhenke Wu, Rui Song
2023-01-31
