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.
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.

本文提出了一种基于因果结构的 VAE 算法 CausalVAE，使用因果层将独立外生因素转化为内生因果因素，实现了因果因素的自动发掘和可解释性，实验结果表明 CausalVAE 提出的因果表示能够在多个数据集上实现对真实因果结构的恢复，同时可以生成对因果结构产生干预的对事实数据。