Abstract: Domain generalization aims to learn knowledge invariant across different
distributions while semantically meaningful for downstream tasks from multiple
source domains, to improve the model's generalization ability on unseen target
domains. The fundamental objective is to understand the underlying "invariance"
behind these observational distributions and such invariance has been shown to
have a close connection to causality. While many existing approaches make use
of the property that causal features are invariant across domains, we consider
the causal invariance of the average causal effect of the features to the
labels. This invariance regularizes our training approach in which
interventions are performed on features to enforce stability of the causal
prediction by the classifier across domains. Our work thus sheds some light on
the domain generalization problem by introducing invariance of the mechanisms
into the learning process. Experiments on several benchmark datasets
demonstrate the performance of the proposed method against SOTAs.