Unsupervised Domain Adaptation for Binary Classification with an Unobservable Source Subpopulation

ArXi:2509.20587v3 Announce Type: replace-cross We study an unsupervised domain adaptation problem where the source domain consists of subpopulations defined by the binary label $Y$ and a binary background (or environment) $A$. We focus on a challenging setting in which one such subpopulation in the source domain is unobservable. Naively ignoring this unobserved group can result in biased estimates and degraded predictive performance. Despite this structured missingness, we show that the prediction in the target domain can still be recovered.