Causal Machine Learning for Treatment Effect Heterogeneity: Interpretation, Benchmarking, Validation

Causal machine learning (ML) has emerged as a promising approach for predicting individualized treatment effects and advancing precision medicine, moving beyond the limitations of conventional one-variable-at-a-time subgroup analyses of randomized trials. A recent benchmarking study by Chen and colleagues evaluated several causal ML learners across two large trials and concluded that current methods do not yet demonstrate reliable performance for clinical application. While such applied methodological investigations are valuable, this conclusion warrants a more nuanced examination. Multiple independent research groups have successfully applied causal ML across respiratory medicine and critical care, identifying clinically meaningful treatment effect heterogeneity that prior subgroup analyses had not resolved, although these applications have methodological limitations and do not yet establish that the detected heterogeneity is robust across internal and external validation. We outline four sequential and iterative dimensions central to credible causal ML analyses: choice of learner, model training with cross-fitting, internal validation using calibration-for-benefit and rank-weighted metrics, and external validation aligned with the original causal question. With careful methodological execution, causal ML holds considerable promise for personalized treatment decisions and warrants continued development; dismissing the field prematurely risks overlooking a powerful tool for personalized treatment decisions.