We propose a general class of algorithms for estimating heterogeneous treatment effects on multiple studies. Our approach, called the multi-study R-learner, generalizes the R-learner to account for between-study heterogeneity and achieves cross-study robustness of confounding adjustment. The multi-study R-learner is flexible in its ability to incorporate many machine learning techniques for estimating heterogeneous treatment effects, nuisance functions, and membership probabilities. We show that the multi-study R-learner treatment effect estimator is asymptotically normal within the series estimation framework. Moreover, we illustrate via realistic cancer data experiments that our approach results in lower estimation error than the R-learner as between-study heterogeneity increases.

翻译：我们提出了一类用于在多项研究中估计异质性处理效应的通用算法。该方法被称为多研究R学习器，它通过泛化R学习器来考虑研究间的异质性，并实现了混杂调整的跨研究稳健性。多研究R学习器具有灵活性，能够整合多种机器学习技术以估计异质性处理效应、干扰函数及成员概率。我们证明，在级数估计框架下，多研究R学习器的处理效应估计量具有渐近正态性。此外，通过基于真实癌症数据的实验，我们表明随着研究间异质性的增加，该方法相比R学习器具有更低的估计误差。