Robust estimation of heterogeneous treatment effects in randomized trials leveraging external data
Journal:
arXiv
Published Date:
Jul 4, 2025
Abstract
Randomized trials are typically designed to detect average treatment effects
but often lack the statistical power to uncover effect heterogeneity over
patient characteristics, 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 has the potential to reduce the CATE
prediction mean squared error while maintaining consistency, even when the
external data is 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 the trial-only learner 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.
