Invariant and Transportable Representations for Anti-Causal Domain Shifts and Victor Veitch Department of Computer Science, University of Chicago Department of Statistics, University of Chicago

Real-world classification problems must contend with domain shift, the (potential) mismatch between the domain where a model is deployed and the domain(s) where the training data was gathered. Methods to handle such problems must specify what structure is common between the domains and what varies. A natural assumption is that causal (structural) relationships are invariant in all domains. Then, it is tempting to learn a predictor for label Y that depends only on its causal parents. However, many real-world problems are "anti-causal" in the sense that Y is a cause of the covariates X--in this case, Y has no causal parents and the naive causal invariance is useless.