Leveraging machine learning to estimate individualized treatment effects in cluster-randomized trials

2026-04-14

AI Summary

Traditional cluster randomized trials typically estimate only the average treatment effect, overlooking heterogeneity at both individual and cluster levels. This work proposes a unified mixed-effects machine learning framework that integrates individual- and cluster-level covariates to estimate conditional average treatment effects while marginalizing over unobserved cluster heterogeneity. For the first time, it systematically combines methods such as Bayesian additive regression trees, multilevel Bayesian causal forests, mixed-effects random forests, mixed-effects gradient boosting, and generalized additive mixed models, incorporating cluster-specific random intercepts to account for within-cluster dependence. The approach is validated through diverse simulation studies and an application to a cluster randomized trial on hypertension management in Ghana, with accompanying open-source code and practical implementation guidelines provided.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Humans and AI: Human-in-the-loop Machine Learning

Application Category

• User Modeling, Personalization and Recommendation: ML for personalized search and recommendations
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

Abstract

Cluster-randomized trials (CRTs) are widely used to evaluate interventions delivered at the clinic, practice, or community level. Although standard analyses typically target average treatment effects, such summaries mask potentially meaningful variation in treatment response across individuals and clusters. This work addresses the estimation of conditional average treatment effects (CATEs) for continuous outcomes in two-arm parallel CRTs by defining causal estimands that incorporate both individual- and cluster-level baseline covariates while marginalizing over unobserved cluster heterogeneity. To estimate these quantities, we develop a unified framework based on mixed-effects machine learning, integrating and extending a range of existing approaches, including Bayesian additive regression trees with random effects, multilevel Bayesian causal forests, mixed-effects random forests, several mixed-effects gradient boosting procedures, and generalized additive mixed models, while incorporating cluster-specific random intercepts to account for within-cluster dependence. We evaluate these methods across diverse simulation scenarios and demonstrate their use in the Task Shifting and Blood Pressure Control in Ghana CRT, which investigates strategies for improving hypertension management. Drawing on these investigations, we provide practical guidance for applying mixed-effects machine learning to quantify treatment-effect heterogeneity in CRTs, together with reproducible code that enables investigators to implement all methods within a coherent workflow.

Problem

• cluster-randomized trials
• individualized treatment effects
• conditional average treatment effects
• treatment-effect heterogeneity
• mixed-effects models

Innovation

• mixed-effects machine learning
• conditional average treatment effects
• cluster-randomized trials
• treatment-effect heterogeneity
• causal inference