This paper introduces a novel approach to anti-causal domain generalization using unlabeled data. It addresses the limitation of existing methods that require labeled data from multiple training environments, making it applicable in data-scarce scenarios. The technique leverages the anti-causal structure where the outcome causes covariates, allowing for regularization of model sensitivity to environmental shifts.
Opening Kapyn…