Unsupervised domain adaptation is critical to many real-world applications
where label information is unavailable in the target domain. In general,
without further assumptions, the joint distribution of the features and the
label is not identifiable in the target domain. To address this issue, we rely
on the property of minimal changes of causal mechanisms across domains to
minimize unnecessary influences of distribution shifts. To encode this
property, we first formulate the data-generating process using a latent
variable model with two partitioned latent subspaces: invariant components
whose distributions stay the same across domains and sparse changing components
that vary across domains. We further constrain the domain shift to have a
restrictive influence on the changing components. Under mild conditions, we
show that the latent variables are partially identifiable, from which it
follows that the joint distribution of data and labels in the target domain is
also identifiable. Given the theoretical insights, we propose a practical
domain adaptation framework called iMSDA. Extensive experimental results reveal
that iMSDA outperforms state-of-the-art domain adaptation algorithms on
benchmark datasets, demonstrating the effectiveness of our framework.

本研究针对无标签信息的目标领域，依靠因果机制的最小变化特性来最小化分布偏移的影响，利用两个分区潜在子空间的潜在变量模型来约束域移，证明潜在变量是部分可识别的，提出一种实用的域适应框架 iMSDA，并在基准数据集上取得了优于现有算法的实验结果，证明了我们框架的有效性。