AI RESEARCH
Improving RCT-Based Treatment Effect Estimation Under Covariate Mismatch via Calibrated Alignment
arXiv CS.LG
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ArXi:2603.19186v1 Announce Type: new Randomized controlled trials (RCTs) are the gold standard for estimating heterogeneous treatment effects, yet they are often underpowered for detecting effect heterogeneity. Large observational studies (OS) can supplement RCTs for conditional average treatment effect (CATE) estimation, but a key barrier is covariate mismatch: the two sources measure different, only partially overlapping, covariates.