Robust estimation of heterogeneous treatment effects in randomized trials leveraging external data

• URL: http://arxiv.org/abs/2507.03681v1
• Date: Fri, 04 Jul 2025 16:01:05 GMT
• Title: Robust estimation of heterogeneous treatment effects in randomized trials leveraging external data
• Authors: Rickard Karlsson, Piersilvio De Bartolomeis, Issa J. Dahabreh, Jesse H. Krijthe,
• Abstract summary: We propose the QR-learner, a model-agnostic learner that estimates conditional average treatment effects (CATE) within the trial population.<n>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.<n>We apply the methods to a real-world dataset, demonstrating improvements in both CATE estimation and statistical power for detecting heterogeneous effects.
• Score: 1.3124513975412255
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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