Reinforcement Learning in Modern Biostatistics: Constructing Optimal Adaptive Interventions

Reinforcement learning (RL) is acquiring a key role in the space of adaptive interventions (AIs), attracting a substantial interest within methodological and theoretical literature and becoming increasingly popular within health sciences. Despite potential benefits, its application in real life is still limited due to several operational and statistical challenges--in addition to ethical and cost issues among others--that remain open in part due to poor communication and synergy between methodological and applied scientists. In this work, we aim to bridge the different domains that contribute to and may benefit from RL, under a unique framework that intersects the areas of RL, causal inference, and AIs, among others. We provide the first unified instructive survey on RL methods for building AIs, encompassing both dynamic treatment regimes (DTRs) and just-in-time adaptive interventions in mobile health (mHealth). We outline similarities and differences between the two areas, and discuss their implications for using RL. We combine our relevant methodological knowledge with motivating studies in both DTRs and mHealth to illustrate the tremendous collaboration opportunities between statistical, RL, and healthcare researchers in the space of AIs.