March 13, 2024, 4:42 a.m. | Katherine Tsai, Stephen R. Pfohl, Olawale Salaudeen, Nicole Chiou, Matt J. Kusner, Alexander D'Amour, Sanmi Koyejo, Arthur Gretton

cs.LG updates on arXiv.org arxiv.org

arXiv:2403.07442v1 Announce Type: new
Abstract: We study the problem of domain adaptation under distribution shift, where the shift is due to a change in the distribution of an unobserved, latent variable that confounds both the covariates and the labels. In this setting, neither the covariate shift nor the label shift assumptions apply. Our approach to adaptation employs proximal causal learning, a technique for estimating causal effects in settings where proxies of unobserved confounders are available. We demonstrate that proxy variables …

abstract apply arxiv assumptions change cs.lg distribution domain domain adaptation labels shift stat.ml study type

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