Learning disentangled representations is important in representation
learning, aiming to learn a low dimensional representation of data where each
dimension corresponds to one underlying generative factor. Due to the
possibility of causal relationships between generative factors, causal
disentangled representation learning has received widespread attention. In this
paper, we first propose new flows that can incorporate causal structure
information into the model, called causal flows. Based on the variational
autoencoders(VAE) commonly used in disentangled representation learning, we
design a new model, CF-VAE, which enhances the disentanglement ability of the
VAE encoder by utilizing the causal flows. By further introducing the
supervision of ground-truth factors, we demonstrate the disentanglement
identifiability of our model. Experimental results on both synthetic and real
datasets show that CF-VAE can achieve causal disentanglement and perform
intervention experiments. Moreover, CF-VAE exhibits outstanding performance on
downstream tasks and has the potential to learn causal structure among factors.

本文提出了一种结合因果结构信息的新型流模型 —— 因果流，设计了 CF-VAE 模型，通过利用因果流增强 VAE 编码器的解缠结能力。进一步引入地面真实因素的监督，证明了该模型的解纠缠可识别性。在合成和真实数据集上的实验结果表明，CF-VAE 可以实现因果解纠缠，并进行干预实验。此外，CF-VAE 在下游任务上表现出优异的性能，并有潜力学习因果因素之间的结构。