AI RESEARCH
Transfer Learning for Meta-analysis Under Covariate Shift
arXiv CS.LG
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ArXi:2604.02656v1 Announce Type: cross Randomized controlled trials often do not represent the populations where decisions are made, and covariate shift across studies can invalidate standard IPD meta-analysis and transport estimators. We propose a placebo-anchored transport framework that treats source-trial outcomes as abundant proxy signals and target-trial placebo outcomes as scarce, high-fidelity gold labels to calibrate baseline risk.