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Estimating heterogeneous treatment effects (HTEs) is crucial for precisionmedicine. While multiple studies can improve the generalizability of results,leveraging them for estimation is statistically challenging. Existingapproaches often assume identical HTEs across studies, but this may be violateddue to various sources of between-study heterogeneity, including differences instudy design, study populations, and data collection protocols, among others.To this end, we propose a framework for multi-study HTE estimation thataccounts for between-study heterogeneity in the nuisance functions andtreatment effects. Our approach, the multi-study R-learner, extends theR-learner to obtain principled statistical estimation with machine learning(ML) in the multi-study setting. It involves a data-adaptive objective functionthat links study-specific treatment effects with nuisance functions throughmembership probabilities, which enable information to be borrowed acrosspotentially heterogeneous studies. The multi-study R-learner framework cancombine data from randomized controlled trials, observational studies, or acombination of both. It's easy to implement and flexible in its ability toincorporate ML for estimating HTEs, nuisance functions, and membershipprobabilities. In the series estimation framework, we show that the multi-studyR-learner is asymptotically normal and more efficient than the R-learner whenthere is between-study heterogeneity in the propensity score model underhomoscedasticity. We illustrate using cancer data that the proposed methodperforms favorably compared to existing approaches in the presence ofbetween-study heterogeneity.

Multi-Study R-Learner for Estimating Heterogeneous Treatment Effects Across Studies Using Statistical Machine Learning