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Adapting to Shifts in Latent Confounders via Observed Concepts and Proxies
Matt Kusner · Ibrahim Alabdulmohsin · Stephen Pfohl · Olawale Salaudeen · Arthur Gretton · Sanmi Koyejo · Jessica Schrouff · Alexander D'Amour

We address the problem of unsupervised domain adaptation when the source differs from the target because of a shift in the distribution of a latent confounder. In this case, neither covariate shift nor label shift assumptions apply. When all data is discrete, we show that the optimal target predictor can be non-parametrically identified with the help of concept and proxy variables, available only in the source, and unlabeled data from the target.

Author Information

Matt Kusner (University College London)
Ibrahim Alabdulmohsin (Google Research)
Stephen Pfohl (Google)
Olawale Salaudeen (Google)
Arthur Gretton (Gatsby Computational Neuroscience Unit)
Sanmi Koyejo (Google / Illinois)
Jessica Schrouff (Google Health)
Alexander D'Amour (Google Brain)

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