C-Disentanglement: Discovering Causally-Independent Generative Factors
  under an Inductive Bias of Confounder
Xiaoyu Liu Jiaxin Yuan Bang An Yuancheng Xu Yifan Yang Furong Huang
Representation learning assumes that real-world data is generated by a few
semantically meaningful generative factors (i.e., sources of variation) and
aims to discover them in the latent space. These factors are expected to be
causally disentangled, meaning that distinct factors are encoded into separate
latent variables, and changes in one factor will not affect the values of the
others. Compared to statistical independence, causal disentanglement allows
more controllable data generation, improved robustness, and better
generalization. However, most existing work assumes unconfoundedness in the
discovery process, that there are no common causes to the generative factors
and thus obtain only statistical independence. In this paper, we recognize the
importance of modeling confounders in discovering causal generative factors.
Unfortunately, such factors are not identifiable without proper inductive bias.
We fill the gap by introducing a framework entitled Confounded-Disentanglement
(C-Disentanglement), the first framework that explicitly introduces the
inductive bias of confounder via labels from domain expertise. In addition, we
accordingly propose an approach to sufficiently identify the causally
disentangled factors under any inductive bias of the confounder. We conduct
extensive experiments on both synthetic and real-world datasets. Our method
demonstrates competitive results compared to various SOTA baselines in
obtaining causally disentangled features and downstream tasks under domain
shifts.