Perturbing Confounders via Causal Disentanglement for Domain Generalization

Jingliang Bian, Junhao Li, Jian Xu, Ruxin Wang

Abstract: Domain Generalization (DG) aims to generalize a model trained on source domains to unseen target domains. Learning domain-invariant representations based on causal inference is one of the popular directions in DG. However, these methods would yield an inaccurate causal variable set due to the lack of heterogeneous domain data or a prior causal structure, which severely weakens their generalization capacity. To this end, we propose a novel DG method called Perturbing Confounders via Causal Disentanglement (PCCD), which explicitly disentangles latent features into causally relevant features and confounding features. The method perturbs the confounding features to improve generalization on unseen domains. Specifically, we first apply the causal disentanglement framework to separate causal features and confounding features. Then, we introduce a learnable perturbation initialized as Gaussian distribution on the batch-wise statistics for each dimension of the confounding features while constraining semantic consistency. Extensive experiments on several benchmarks indicate that our framework achieves state-of-the-art performance compared to other competing methods.

External IDs:dblp:conf/icmcs/BianLXW25