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.

翻译：表示学习假设现实世界数据由少量语义上有意义的生成因子（即变异来源）生成，并旨在潜在空间中发现它们。这些因子预期是因果解耦的，即不同因子被编码为独立的潜在变量，且一个因子的变化不会影响其他因子的值。与统计独立性相比，因果解耦允许更可控的数据生成、更强的鲁棒性以及更好的泛化能力。然而，现有大多数工作假设发现过程中无混淆效应，即生成因子间不存在共同原因，因此仅获得统计独立性。本文认识到在发现因果生成因子时对混淆因子建模的重要性。遗憾的是，若无恰当的归纳偏置，此类因子无法被识别。为此，我们引入名为“混淆-解耦”（C-Disentanglement）的框架，这是首个通过领域专业知识标签显式引入混淆因子归纳偏置的框架。此外，我们相应提出了一种方法，能够在任意混淆因子归纳偏置下充分识别因果解耦因子。我们在合成数据集和真实数据集上进行了广泛实验。我们的方法在获得因果解耦特征以及领域迁移下的下游任务中，相较于多种SOTA基线展现出具有竞争力的结果。