Anti-Causal Domain Generalization: Leveraging Unlabeled Data
This paper studies domain generalization in an anti-causal setting where the outcome causes the covariates. The authors propose two methods that leverage unlabeled data from multiple environments to regularize the model's sensitivity to changes in the mean and covariance of covariates, with worst-case optimality guarantees. Empirical results are shown on a controlled physical system and a physiological signal dataset.
content type paperpublished July 2026
Anti-Causal Domain Generalization: Leveraging Unlabeled Data
AuthorsSorawit Saengkyongam†, Juan L. Gamella, Andrew C. Miller†, Jonas Peters‡, Nicolai Meinshausen‡, Christina Heinze-Deml†
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The problem of domain generalization concerns learning predictive models that are robust to distribution shifts when deployed in new, previously unseen environments. Existing methods typically require labeled data from multiple training environments, limiting their applicability when labeled data are scarce. In this work, we study domain generalization in an anti-causal setting, where the outcome causes the observed covariates. Under this structure, environment perturbations that affect the covariates do not propagate to the outcome, which motivates regularizing the model’s sensitivity to these perturbations. Crucially, estimating these perturbation directions does not require labels, enabling us to leverage unlabeled data from multiple environments. We propose two methods that penalize the model’s sensitivity to variations in the mean and covariance of the covariates across environments, respectively, and prove that these methods have worst-case optimality guarantees under certain classes of environments. Finally, we demonstrate the empirical performance of our approach on a controlled physical system and a physiological signal dataset.
† Apple
‡ ETH Zürich
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*=Equal Contributors
This paper was accepted at the workshop “Trustworthy Machine Learning for Healthcare Workshop” at the conference ICLR 2023.
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