  Studies show that the representations learned by deep neural networks can be
transferred to similar prediction tasks in other domains for which we do not
have enough labeled data. However, as we transition to higher layers in the
model, the representations become more task-specific and less generalizable.
Recent research on deep domain adaptation proposed to mitigate this problem by
forcing the deep model to learn more transferable feature representations
across domains. This is achieved by incorporating domain adaptation methods
into deep learning pipeline. The majority of existing models learn the
transferable feature representations which are highly correlated with the
outcome. However, correlations are not always transferable. In this paper, we
propose a novel deep causal representation learning framework for unsupervised
domain adaptation, in which we propose to learn domain-invariant causal
representations of the input from the source domain. We simulate a virtual
target domain using reweighted samples from the source domain and estimate the
causal effect of features on the outcomes. The extensive comparative study
demonstrates the strengths of the proposed model for unsupervised domain
adaptation via causal representations.
