Disentangled representation learning aims to learn low-dimensional representations of data, where each dimension corresponds to an underlying generative factor. Currently, Variational Auto-Encoder (VAE) are widely used for disentangled representation learning, with the majority of methods assuming independence among generative factors. However, in real-world scenarios, generative factors typically exhibit complex causal relationships. We thus design a new VAE-based framework named Disentangled Causal Variational Auto-Encoder (DCVAE), which includes a variant of autoregressive flows known as causal flows, capable of learning effective causal disentangled representations. We provide a theoretical analysis of the disentanglement identifiability of DCVAE, ensuring that our model can effectively learn causal disentangled representations. The performance of DCVAE is evaluated on both synthetic and real-world datasets, demonstrating its outstanding capability in achieving causal disentanglement and performing intervention experiments. Moreover, DCVAE exhibits remarkable performance on downstream tasks and has the potential to learn the true causal structure among factors.

翻译：解耦表示学习旨在学习数据的低维表示，其中每个维度对应一个潜在生成因子。目前，变分自编码器（VAE）被广泛用于解耦表示学习，大多数方法假设生成因子之间相互独立。然而，在现实场景中，生成因子通常表现出复杂的因果关系。因此，我们设计了一种新的基于VAE的框架，命名为解耦因果变分自编码器（DCVAE），该框架包含一种称为因果流的自回归流变体，能够学习有效的因果解耦表示。我们对DCVAE的解耦可辨识性进行了理论分析，确保我们的模型能够有效学习因果解耦表示。DCVAE的性能在合成和真实数据集上均得到了评估，展示了其在实现因果解耦和执行干预实验方面的卓越能力。此外，DCVAE在下游任务中表现出色，并具有学习因子间真实因果结构的潜力。