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

arXiv: https://arxiv.org/abs/2312.06403 · HTML: https://mlsanity.com/paper/2312.06403/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 43 references that are in the map.
- First posted on arXiv 11 Dec 2023.

## Builds on (most cited first, 18 of 18)

- [Quasi-Oracle Estimation of Heterogeneous Treatment Effects](https://mlsanity.com/paper/1712.04912/) (2017)
- [Scalable Bayesian Optimization Using Deep Neural Networks](https://mlsanity.com/paper/1502.05700/) (2015)
- [Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson Sampling](https://mlsanity.com/paper/1802.09127/) (2018)
- [River: machine learning for streaming data in Python](https://mlsanity.com/paper/2012.04740/) (2020)
- [A Gang of Bandits](https://mlsanity.com/paper/1306.0811/) (2013)
- [IntelligentPooling: Practical Thompson Sampling for mHealth](https://mlsanity.com/paper/2008.01571/) (2020)
- [Action Centered Contextual Bandits](https://mlsanity.com/paper/1711.03596/) (2017)
- [Doubly robust Thompson sampling for linear payoffs](https://mlsanity.com/paper/2102.01229/) (2021)
- [Semiparametric Contextual Bandits](https://mlsanity.com/paper/1803.04204/) (2018)
- [Laplacian-regularized graph bandits: Algorithms and theoretical analysis](https://mlsanity.com/paper/1907.05632/) (2019)
- [Mixed-Effect Thompson Sampling](https://mlsanity.com/paper/2205.15124/) (2022)
- [Double Doubly Robust Thompson Sampling for Generalized Linear Contextual Bandits](https://mlsanity.com/paper/2209.06983/) (2022)
- [Debiased Machine Learning without Sample-Splitting for Stable Estimators](https://mlsanity.com/paper/2206.01825/) (2022)
- [Stochastic Gradient Trees](https://mlsanity.com/paper/1901.07777/) (2019)
- [Contextual Multi-armed Bandit Algorithm for Semiparametric Reward Model](https://mlsanity.com/paper/1901.11221/) (2019)
- [Personalized Policy Learning using Longitudinal Mobile Health Data](https://mlsanity.com/paper/2001.03258/) (2020)
- [Horde of Bandits using Gaussian Markov Random Fields](https://mlsanity.com/paper/1703.02626/) (2017)
- [Using dynamical quantization to perform split attempts in online tree regressors](https://mlsanity.com/paper/2012.00083/) (2020)

## Built on by (most cited first, 5 of 5)

- [Generator-Mediated Bandits: Thompson Sampling for GenAI-Powered Adaptive Interventions](https://mlsanity.com/paper/2505.16311/) (2025)
- [Identifiable Latent Bandits: Leveraging observational data for personalized decision-making](https://mlsanity.com/paper/2407.16239/) (2024)
- [Co-Exploration and Co-Exploitation via Shared Structure in Multi-Task Bandits](https://mlsanity.com/paper/2512.12693/) (2025)
- [Calibration-Gated LLM Pseudo-Observations for Online Contextual Bandits](https://mlsanity.com/paper/2604.14961/) (2026)
- [Optimizing Digital Therapeutic Interventions: Online Learning under Endogenous Adherence](https://mlsanity.com/paper/2605.24261/) (2026)

## Abstract (arXiv)

Mobile health leverages personalized and contextually tailored interventions optimized through bandit and reinforcement learning algorithms. In practice, however, challenges such as participant heterogeneity, nonstationarity, and nonlinear relationships hinder algorithm performance. We propose RoME, a Robust Mixed-Effects contextual bandit algorithm that simultaneously addresses these challenges via (1) modeling the differential reward with user- and time-specific random effects, (2) network cohesion penalties, and (3) debiased machine learning for flexible estimation of baseline rewards. We establish a high-probability regret bound that depends solely on the dimension of the differential-reward model, enabling us to achieve robust regret bounds even when the baseline reward is highly complex. We demonstrate the superior performance of the RoME algorithm in a simulation and two off-policy evaluation studies.