Anti-Causal Domain Generalization: Leveraging Unlabeled Data

·2026.07.02 09:00

Key point

This work proposes an Anti-Causal Domain Generalization approach that learns robust predictive models for new environments by leveraging unlabeled data.

Details

Existing Domain Generalization methodologies aim to train models to operate robustly even in new environments, but they typically have the limitation of requiring Labeled Data from multiple training environments.

This study focuses on the Anti-causal setting, in which the outcome influences the observed covariates. In such a structure, changes in the environment affect the covariates but do not affect the outcome.

By leveraging this causal structure, the study shows that regularizing the model so that it does not react excessively sensitively to changes in the covariates enables effective domain generalization using only unlabeled data.

machine-learning domain-generalization anti-causal-learning unlabeled-data

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