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

TL;DR

This paper introduces the QR-learner, a robust method that leverages external data to improve the estimation of individual treatment effects in randomized trials, enhancing personalized decision-making.

Contribution

The paper proposes the QR-learner, a novel, model-agnostic approach that combines trial data with external data to accurately estimate heterogeneous treatment effects.

Findings

QR-learner reduces mean squared error compared to trial-only methods

The method guarantees recovery of true CATE even with misaligned external data

Application to real-world data shows improved detection of treatment heterogeneity

Abstract

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.