Off-Policy Estimation of Long-Term Average Outcomes With Applications to Mobile Health. Issue 533 (2nd March 2021)

• Record Type: Journal Article
• Authors: Liao, Peng; Klasnja, Predrag; Murphy, Susan
• Is Part Of: Journal of the American Statistical Association. Volume 116:Issue 533(2021)
• Page Start: 382
• Page End: 391
• Publication Date: 2021-03-02
• Subjects: Markov decision process -- Policy evaluation -- Reinforcement learning -- Sequential decision making; Statistics -- Periodicals; Statistics -- Periodicals; Statistiques -- Périodiques; États-Unis -- Statistiques -- Périodiques; 519.5
• DOI: 10.1080/01621459.2020.1807993
• Languages: English
• ISSNs: 0162-1459

Abstract

Abstract: Due to the recent advancements in wearables and sensing technology, health scientists are increasingly developing mobile health (mHealth) interventions. In mHealth interventions, mobile devices are used to deliver treatment to individuals as they go about their daily lives. These treatments are generally designed to impact a near time, proximal outcome such as stress or physical activity. The mHealth intervention policies, often called just-in-time adaptive interventions, are decision rules that map an individual's current state (e.g., individual's past behaviors as well as current observations of time, location, social activity, stress, and urges to smoke) to a particular treatment at each of many time points. The vast majority of current mHealth interventions deploy expert-derived policies. In this article, we provide an approach for conducting inference about the performance of one or more such policies using historical data collected under a possibly different policy. Our measure of performance is the average of proximal outcomes over a long time period should the particular mHealth policy be followed. We provide an estimator as well as confidence intervals. This work is motivated by HeartSteps, an mHealth physical activity intervention. Supplementary materials for this article are available online.