Learning disentanglement aims at finding a low dimensional representation which consists of multiple explanatory and generative factors of the observational data. The framework of variational autoencoder (VAE) is commonly used to disentangle independent factors from observations. However, in real scenarios, factors with semantics are not necessarily independent. Instead, there might be an underlying causal structure which renders these factors dependent. We thus propose a new VAE based framework named CausalVAE, which includes a Causal Layer to transform independent exogenous factors into causal endogenous ones that correspond to causally related concepts in data. We further analyze the model identifiabitily, showing that the proposed model learned from observations recovers the true one up to a certain degree by providing supervision signals (e.g. feature labels). Experiments are conducted on various datasets, including synthetic and real word benchmark CelebA. Results show that the causal representations learned by CausalVAE are semantically interpretable, and their causal relationship as a Directed Acyclic Graph (DAG) is identified with good accuracy. Furthermore, we demonstrate that the proposed CausalVAE model is able to generate counterfactual data through "do-operation" to the causal factors.

翻译：解耦学习旨在寻找一个由观测数据的多个可解释生成因素组成的低维表示。变分自编码器（VAE）框架通常用于从观测中解耦独立因素。然而，在实际场景中，具有语义的因素不一定相互独立，反而可能存在潜在的因果结构使这些因素相互依赖。为此，我们提出了一种基于VAE的新框架——CausalVAE，其中包含一个因果层，用于将独立的外生因素转化为对应数据中因果相关概念的内生因果因素。我们进一步分析了模型的可辨识性，表明通过提供监督信号（如特征标签），从观测中学习的所提模型能够在一定程度上恢复真实因果结构。我们在包括合成数据和真实基准CelebA在内的多种数据集上进行了实验。结果表明，CausalVAE学习到的因果表示具有语义可解释性，其作为有向无环图（DAG）的因果关系能够以较高精度被识别。此外，我们证明所提出的CausalVAE模型能够通过对因果因素执行“干预操作”生成反事实数据。