Randomized trials are typically designed to detect average treatment effects but often lack the statistical power to uncover individual-level treatment effect heterogeneity, limiting their value for personalized decision-making. To address this, we propose the QR-learner, a model-agnostic learner that estimates conditional average treatment effects (CATE) within the trial population by leveraging external data from other trials or observational studies. The proposed method is robust: it can reduce the mean squared error relative to a trial-only CATE learner, and is guaranteed to recover the true CATE even when the external data are not aligned with the trial. Moreover, we introduce a procedure that combines the QR-learner with a trial-only CATE learner and show that it asymptotically matches or exceeds both component learners in terms of mean squared error. We examine the performance of our approach in simulation studies and apply the methods to a real-world dataset, demonstrating improvements in both CATE estimation and statistical power for detecting heterogeneous effects.

翻译：随机试验通常旨在检测平均治疗效应，但往往缺乏揭示个体层面治疗效应异质性的统计功效，从而限制了其在个性化决策中的价值。为解决这一问题，我们提出QR-learner——一种模型无关的学习器，通过利用来自其他试验或观察性研究的外部数据，在试验人群中估计条件平均治疗效应（CATE）。所提方法具有稳健性：相较于仅使用试验数据的CATE学习器，它能降低均方误差，并且即使外部数据与试验数据不一致，也能保证恢复真实的CATE。此外，我们引入了一种将QR-learner与仅使用试验数据的CATE学习器相结合的程序，并证明其在均方误差方面渐近匹配或优于两个组件学习器。我们通过模拟研究检验了该方法的性能，并将其应用于真实世界数据集，证明了其在CATE估计和检测异质性效应的统计功效方面均有所提升。