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There is a growing interest in using reinforcement learning (RL) topersonalize sequences of treatments in digital health to support users inadopting healthier behaviors. Such sequential decision-making problems involvedecisions about when to treat and how to treat based on the user's context(e.g., prior activity level, location, etc.). Online RL is a promisingdata-driven approach for this problem as it learns based on each user'shistorical responses and uses that knowledge to personalize these decisions.However, to decide whether the RL algorithm should be included in an``optimized'' intervention for real-world deployment, we must assess the dataevidence indicating that the RL algorithm is actually personalizing thetreatments to its users. Due to the stochasticity in the RL algorithm, one mayget a false impression that it is learning in certain states and using thislearning to provide specific treatments. We use a working definition ofpersonalization and introduce a resampling-based methodology for investigatingwhether the personalization exhibited by the RL algorithm is an artifact of theRL algorithm stochasticity. We illustrate our methodology with a case study byanalyzing the data from a physical activity clinical trial called HeartSteps,which included the use of an online RL algorithm. We demonstrate how ourapproach enhances data-driven truth-in-advertising of algorithm personalizationboth across all users as well as within specific users in the study.

Did we personalize? Assessing personalization by an online reinforcement learning algorithm using resampling