Transporting Causal Mechanisms for Unsupervised Domain Adaptation
This addresses the domain adaptation problem for machine learning practitioners by providing a causal approach to improve model transfer across domains, though it builds incrementally on existing causal theories.
The paper tackled the problem of semantic loss in unsupervised domain adaptation by identifying it as a confounding effect that requires causal intervention, and proposed a practical method that achieved state-of-the-art performance on benchmarks like ImageCLEF-DA, Office-Home, and VisDA-2017.
Existing Unsupervised Domain Adaptation (UDA) literature adopts the covariate shift and conditional shift assumptions, which essentially encourage models to learn common features across domains. However, due to the lack of supervision in the target domain, they suffer from the semantic loss: the feature will inevitably lose non-discriminative semantics in source domain, which is however discriminative in target domain. We use a causal view -- transportability theory -- to identify that such loss is in fact a confounding effect, which can only be removed by causal intervention. However, the theoretical solution provided by transportability is far from practical for UDA, because it requires the stratification and representation of the unobserved confounder that is the cause of the domain gap. To this end, we propose a practical solution: Transporting Causal Mechanisms (TCM), to identify the confounder stratum and representations by using the domain-invariant disentangled causal mechanisms, which are discovered in an unsupervised fashion. Our TCM is both theoretically and empirically grounded. Extensive experiments show that TCM achieves state-of-the-art performance on three challenging UDA benchmarks: ImageCLEF-DA, Office-Home, and VisDA-2017. Codes are available in Appendix.