  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.
