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Disentangled representation learning aims to learn a low dimensionalrepresentation of data where each dimension corresponds to one underlyinggenerative factor. Due to the causal relationships between generative factorsin real-world situations, causal disentangled representation learning hasreceived widespread attention. In this paper, we first propose a variant ofautoregressive flows, called causal flows, which incorporate true causalstructure of generative factors into the flows. Then, we design a new VAE modelbased on causal flows named Causal Flows Variational Autoencoders (CauF-VAE) tolearn causally disentangled representations. We provide a theoretical analysisof the disentanglement identifiability of CauF-VAE by incorporating supervisedinformation on the ground-truth factors. The performance of CauF-VAE isevaluated on both synthetic and real datasets, showing its capability ofachieving causal disentanglement and performing intervention experiments.Moreover, CauF-VAE exhibits remarkable performance on downstream tasks and hasthe potential to learn true causal structure among factors.

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