Learning When the Concept Shifts: Confounding, Invariance, and Dimension Reduction

ArXi:2406.15904v2 Announce Type: replace Practitioners often face the challenge of deploying prediction models in new environments with shifted distributions of covariates and responses. With observational data, such shifts are often driven by unobserved confounding, and can in fact alter the concept of which model is best. This paper studies distribution shifts in the domain adaptation problem with unobserved confounding.