  Semi-supervised domain adaptation (SSDA) adapts a learner to a new domain by
effectively utilizing source domain data and a few labeled target samples. It
is a practical yet under-investigated research topic. In this paper, we analyze
the SSDA problem from two perspectives that have previously been overlooked,
and correspondingly decompose it into two \emph{key subproblems}: \emph{robust
domain adaptation (DA) learning} and \emph{maximal cross-domain data
utilization}. \textbf{(i)} From a causal theoretical view, a robust DA model
should distinguish the invariant ``concept'' (key clue to image label) from the
nuisance of confounding factors across domains. To achieve this goal, we
propose to generate \emph{concept-invariant samples} to enable the model to
classify the samples through causal intervention, yielding improved
generalization guarantees; \textbf{(ii)} Based on the robust DA theory, we aim
to exploit the maximal utilization of rich source domain data and a few labeled
target samples to boost SSDA further. Consequently, we propose a
collaboratively debiasing learning framework that utilizes two complementary
semi-supervised learning (SSL) classifiers to mutually exchange their unbiased
knowledge, which helps unleash the potential of source and target domain
training data, thereby producing more convincing pseudo-labels. Such obtained
labels facilitate cross-domain feature alignment and duly improve the invariant
concept learning. In our experimental study, we show that the proposed model
significantly outperforms SOTA methods in terms of effectiveness and
generalisability on SSDA datasets.
