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

ArXi:2507.03681v3 Announce Type: replace-cross 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.