Unsupervised Domain Adaptation Via Causal-Contrastive Learning

Unsupervised domain adaptation (UDA) aims to reduce the domain differences between source and target domains by mapping their data to a shared feature space, thereby learning domain-invariant features. The aim of this study is to address the challenges faced by contrastive learning-based UDA methods when dealing with domain discrepancies, particularly the spurious correlations introduced by confounding factors caused by data augmentation. In recent years, contrastive learning has gained attention for its powerful representation learning capabilities, as it can pull similar samples from the source and target domains closer together while separating different classes of negative samples. This process helps alleviate domain differences and enhances the model’s generalization ability. However, mainstream UDA methods based on contrastive learning often introduce confounding factors due to the randomness of data augmentation, leading the model to learn incorrect spurious associations, especially when the target domain contains counterfactual data from the source domain. As the amount of counterfactual data increases, this bias and accuracy loss can significantly exacerbate and are difficult to eliminate through non-causal methods. To address this, this paper proposes causal invariance contrastive adaptation (CICA), a causal-contrastive learning-based unsupervised domain adaptation model for image classification. The model inputs labeled source domain samples and unlabeled target domain samples into a feature generator after data augmentation, and quantifies the degree of confusion between the generated features based on a backdoor criterion. We effectively separate domain-invariant features from spurious features using adversarial training, thereby reducing the interference of confounding factors on the domain adaptation task. Our experiments conducted on four domain adaptation image classification benchmark datasets and one counterfactual dataset show that the model achieves a significant improvement in average classification accuracy compared to state-of-the-art methods on the benchmark datasets, while still maintaining advanced performance on the counterfactual dataset.