Learning disentangled causal representations is a challenging problem that has gained significant attention recently due to its implications for extracting meaningful information for downstream tasks. In this work, we define a new notion of causal disentanglement from the perspective of independent causal mechanisms. We propose ICM-VAE, a framework for learning causally disentangled representations supervised by causally related observed labels. We model causal mechanisms using learnable flow-based diffeomorphic functions to map noise variables to latent causal variables. Further, to promote the disentanglement of causal factors, we propose a causal disentanglement prior that utilizes the known causal structure to encourage learning a causally factorized distribution in the latent space. Under relatively mild conditions, we provide theoretical results showing the identifiability of causal factors and mechanisms up to permutation and elementwise reparameterization. We empirically demonstrate that our framework induces highly disentangled causal factors, improves interventional robustness, and is compatible with counterfactual generation.

翻译：学习因果解耦表示是一个具有挑战性的问题，近年来因其对下游任务中提取有意义信息的潜力而受到广泛关注。本文从独立因果机制的角度定义了因果解耦的新概念，并提出了ICM-VAE框架——一种通过因果相关的观测标签监督学习因果解耦表示的方法。我们利用可学习的流形微分同胚函数对因果机制进行建模，将噪声变量映射为潜在因果变量。此外，为促进因果因子的解耦，我们提出了一种因果解耦先验，该先验利用已知因果结构鼓励在潜在空间中学习因果因子化分布。在相对宽松的条件下，我们提供了理论结果，证明了因果因子与机制在置换和逐元素重参数化意义下的可辨识性。实验表明，我们的框架能生成高度解耦的因果因子、提升干预鲁棒性，并支持反事实生成。