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处理效应，但往往缺乏揭示个体层面处理效应异质性的统计效能，从而限制了其在个性化决策中的应用价值。为应对这一挑战，我们提出QR学习器——一种模型无关的学习方法，通过利用来自其他试验或观察性研究的外部数据，估算试验人群内的条件平均处理效应。该方法具有稳健性：相较于仅基于试验数据的条件平均处理效应学习器，它能够降低均方误差，并且即使外部数据与试验数据不一致，仍能确保恢复真实的条件平均处理效应。此外，我们引入一个将QR学习器与仅基于试验数据的条件平均处理效应学习器相结合的程序，并证明其在均方误差上渐近地匹配或超越两种单独学习器。通过模拟研究评估方法性能，并将该方法应用于真实数据集，我们展示了在条件平均处理效应估计和检测异质性效应的统计效能方面的改进。