CF-VAE: Causal Disentangled Representation Learning with VAE and Causal Flows

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 a 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.