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 is commonly used to disentangle independent factors from observations. However, in real scenarios, the factors with semantic meanings are not necessarily independent. Instead, there might be an underlying causal structure due to physics laws. We thus propose a new VAE based framework named CausalVAE, which includes causal layers to transform independent factors into causal factors that correspond to causally related concepts in data. We analyze the model identifiabitily of CausalVAE, showing that the generative model learned from the observational data recovers the true one up to a certain degree. Experiments are conducted on various datasets, including synthetic datasets consisting of pictures with multiple causally related objects abstracted from physical world, and a benchmark face dataset CelebA. The results show that the causal representations by CausalVAE are semantically interpretable, and lead to better results on downstream tasks. The new framework allows causal intervention, by which we can intervene any causal concepts to generate artificial data.

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。

CausalVAE: 变分自编码器中的结构因果解缠