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[2602.17187] Anti-causal domain generalization: Leveraging unlabeled data
Summary
The paper explores anti-causal domain generalization, proposing methods to leverage unlabeled data for robust predictive modeling in varying environments.
Why It Matters
This research addresses the challenge of domain generalization, particularly in scenarios where labeled data is scarce. By focusing on anti-causal relationships, the authors present innovative techniques that enhance model robustness, which is crucial for real-world applications in machine learning.
Key Takeaways
- Introduces anti-causal domain generalization for robust modeling.
- Proposes methods that utilize unlabeled data from multiple environments.
- Demonstrates empirical performance on real-world datasets.
- Methods include penalizing model sensitivity to covariate variations.
- Provides worst-case optimality guarantees under specific conditions.
Statistics > Machine Learning arXiv:2602.17187 (stat) [Submitted on 19 Feb 2026] Title:Anti-causal domain generalization: Leveraging unlabeled data Authors:Sorawit Saengkyongam, Juan L. Gamella, Andrew C. Miller, Jonas Peters, Nicolai Meinshausen, Christina Heinze-Deml View a PDF of the paper titled Anti-causal domain generalization: Leveraging unlabeled data, by Sorawit Saengkyongam and 5 other authors View PDF Abstract: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 ...
