Estimating and discovering heterogeneous treatment effects using machine learning in epidemiological studies: a
Toshiaki Komura1,2,3, Falco J Bargagli-Stoffi4,5, Onyebuchi A Arah5,6,7,8
1Department of Social and Behavioral Sciences, Harvard T. H. Chan School of Public Health, Boston, MA, 02115, United States.
International Journal of Epidemiology
|June 13, 2026
Summary
This guide explains machine learning for heterogeneous treatment effect (HTE) analysis, focusing on conditional average treatment effect (CATE) estimation. It provides practical tools and codes for researchers in epidemiological studies.
Area of Science:
- Epidemiology
- Biostatistics
- Health Informatics
Background:
- Machine learning for heterogeneous treatment effect (HTE) estimation is gaining traction in healthcare.
- Meta-learner frameworks and causal forests are popular for conditional average treatment effect (CATE) estimation.
Purpose of the Study:
- To provide a practical guide and statistical codes for implementing machine learning-based HTE analysis.
- To equip researchers with conceptual understanding and practical tools for HTE analysis in epidemiological research.
Main Methods:
- Overview of core motivations for HTE analysis.
- Methodological descriptions of machine learning algorithms for CATE estimation.
- Guidance on calibrating model fit for HTE models.
Main Results:
- Demonstration of HTE analysis using a national sample of US older adults.
- Discussion of practical considerations for HTE analysis with granular CATE.
- Exploration of HTE assessment, scale, reference point, and CATE interpretation.
Conclusions:
- This paper empowers researchers to apply machine learning-based HTE analysis in both randomized controlled trials and observational studies.
- Provides essential conceptual and practical resources for advancing HTE research in epidemiology.
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