ArticleJournal of the American Statistical Association2021
Off-Policy Estimation of Long-Term Average Outcomes with Applications to Mobile Health.
Article in Journal of the American Statistical Association, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- A bibliometric analysis of research on the application of just-in-time adaptive interventions in mental health.Medicine · 2026Review
- Informing the Design of Individualized Self-Management Regimens from the Human, Data, and Machine Learning Perspectives.ACM transactions on computer-human interaction : a publication of the Association for Computing Machinery · 2025Article
- MULTIVARIATE DYNAMIC MEDIATION ANALYSIS UNDER A REINFORCEMENT LEARNING FRAMEWORK.Annals of statistics · 2025Article
- Asymptotic Inference for Multi-Stage Stationary Treatment Policy with Variable Selection.Journal of machine learning research : JMLR · 2025Article
- Optimizing Contingency Management with Reinforcement Learning.medRxiv : the preprint server for health sciences · 2024Article
- BATCH POLICY LEARNING IN AVERAGE REWARD MARKOV DECISION PROCESSES.Annals of statistics · 2022Article
- Analyzing Patient Trajectories With Artificial Intelligence.Journal of medical Internet research · 2021Article
Corrections and comments
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Authors and funding
3 authors.
Funding
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 a 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 paper, 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.