Replicable Bandits for Digital Health Interventions
Kelly W. Zhang, Nowell Closser, Anna L. Trella, Susan A. Murphy

TL;DR
This paper investigates the challenges of statistical inference in adaptive digital health trials, demonstrating that standard estimators can be inconsistent, and proposes the concept of replicable bandit algorithms to ensure reliable, reproducible results.
Contribution
The paper introduces the formal concept of replicable bandit algorithms and proves their effectiveness in ensuring consistent and asymptotically normal estimators in adaptive trials.
Findings
Standard estimators can be inconsistent in adaptive trials.
Replicable bandit algorithms guarantee estimator consistency.
Simulation studies validate theoretical results.
Abstract
Adaptive treatment assignment algorithms, such as bandit algorithms, are increasingly used in digital health intervention clinical trials. Frequently, the data collected from these trials is used to conduct causal inference and related data analyses to decide how to refine the intervention, and whether to roll-out the intervention more broadly. This work studies inference for estimands that depend on the adaptive algorithm itself; a simple example is the mean reward under the adaptive algorithm. Specifically, we investigate the replicability of statistical analyses concerning such estimands when using data from trials deploying adaptive treatment assignment algorithms. We demonstrate that many standard statistical estimators can be inconsistent and fail to be replicable across repetitions of the clinical trial, even as the sample size grows large. We show that this non-replicability is…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAdvanced Bandit Algorithms Research · Mobile Health and mHealth Applications · Digital Mental Health Interventions
