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Mobile health leverages personalized and contextually tailored interventionsoptimized through bandit and reinforcement learning algorithms. In practice,however, challenges such as participant heterogeneity, nonstationarity, andnonlinear relationships hinder algorithm performance. We propose RoME, a RobustMixed-Effects contextual bandit algorithm that simultaneously addresses thesechallenges via (1) modeling the differential reward with user- andtime-specific random effects, (2) network cohesion penalties, and (3) debiasedmachine learning for flexible estimation of baseline rewards. We establish ahigh-probability regret bound that depends solely on the dimension of thedifferential-reward model, enabling us to achieve robust regret bounds evenwhen the baseline reward is highly complex. We demonstrate the superiorperformance of the RoME algorithm in a simulation and two off-policy evaluationstudies.

RoME: A Robust Mixed-Effects Bandit Algorithm for Optimizing Mobile Health Interventions