Robust estimation of heterogeneous treatment effects in randomized trials leveraging external data
This addresses the challenge of personalized decision-making in medical or policy contexts by providing a robust method to estimate heterogeneous treatment effects, though it appears incremental as it builds on existing CATE learners with external data integration.
The paper tackles the problem of estimating individual-level treatment effect heterogeneity in randomized trials, which often lack statistical power, by proposing the QR-learner that leverages external data to improve conditional average treatment effect (CATE) estimation, showing reductions in mean squared error and enhanced statistical power in simulations and real-world applications.
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.