  Domain adaptation solves the learning problem in a target domain by
leveraging the knowledge in a relevant source domain. While remarkable advances
have been made, almost all existing domain adaptation methods heavily require
large amounts of unlabeled target domain data for learning domain invariant
representations to achieve good generalizability on the target domain. In fact,
in many real-world applications, target domain data may not always be
available. In this paper, we study the cases where at the training phase the
target domain data is unavailable and only well-labeled source domain data is
available, called robust domain adaptation. To tackle this problem, under the
assumption that causal relationships between features and the class variable
are robust across domains, we propose a novel Causal AutoEncoder (CAE), which
integrates deep autoencoder and causal structure learning into a unified model
to learn causal representations only using data from a single source domain.
Specifically, a deep autoencoder model is adopted to learn low-dimensional
representations, and a causal structure learning model is designed to separate
the low-dimensional representations into two groups: causal representations and
task-irrelevant representations. Using three real-world datasets the extensive
experiments have validated the effectiveness of CAE compared to eleven
state-of-the-art methods.
