Estimating and discovering heterogeneous treatment effects using machine learning in epidemiological studies: a practical guide

2026/04/17 by Toshiaki Komura, Falco J. Bargagli-Stoffi, Onyebuchi A. Arah +1 · 1 voice
Mathematics · Medicine · #Advanced Causal Inference Techniques #Statistical Methods in Epidemiology #Artificial Intelligence in Healthcare and Education

paper · doi:10.1093/ije/dyag092

openalex publication_date 2026/04/17 · openalex created_date 2026/06/15 · openalex updated_date 2026/07/27

Abstract

Machine learning-based heterogeneous treatment effect (HTE) estimation and discovery have recently received substantial attention in the healthcare literature. In particular, meta-learner frameworks and causal forests have been widely used in estimating the conditional average treatment effect (CATE). Such advances in HTE estimation and discovery have allowed researchers to assess HTE patterns in their data. Here, we provide a comprehensive and practical guide as well as statistical codes for researchers to implement these models effectively. Specifically, we provide an overview of core motivations for HTE analysis. Then, we describe a methodological overview of popular machine learning algorithms for CATE estimation and how to calibrate their model fit. After demonstrating an application example using a national sample of US older adults, we discuss some critical and practical considerations of HTE analysis with highly granular CATE. Finally, we discuss the assessment of HTE, including the scale and reference point, as well as the interpretation of CATE. Overall, this paper aims to equip researchers with both the conceptual understanding and practical tools necessary to apply machine learning-based HTE analysis in epidemiological research, including both randomized controlled trials and observational studies.

Citations

• Applied Causal Inference Powered by ML and AI
• Personalized Decision Making -- A Conceptual Introduction
• Estimating subgroup effects in generalizability and transportability\n analyses
• Causal Rule Ensemble: Interpretable Discovery and Inference of Heterogeneous Treatment Effects
• Towards optimal doubly robust estimation of heterogeneous causal effects
• Group Average Treatment Effects for Observational Studies
• Estimating Treatment Effects with Causal Forests: An Application
• Quasi-Oracle Estimation of Heterogeneous Treatment Effects
• Quasi-oracle estimation of heterogeneous treatment effects
• Estimation and Inference of Heterogeneous Treatment Effects using Random Forests
• Estimation and Inference of Heterogeneous Treatment Effects using Random Forests
• Bayesian Nonparametric Modeling for Causal Inference
• Practical Guide to Honest Causal Forests for Identifying Heterogeneous Treatment Effects

Discussions

• How can epidemiologists use machine learning to estimate who benefits most from an intervention? A new paper by IQSS grad affiliate Toshiaki Komura and co-authors offers guidance on heterogeneous tre [bsky, 2 points, 0 comments]

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