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.

本文提出了一种基于独立因果机制的因果解缠结的新概念，并提出了 ICM-VAE 框架来学习因果解缠结表示，使用可学习的基于流的差分同胚函数将噪声变量映射到潜在因果变量，同时为了促进因果因素的解缠结，提出了因果解缠结先验。在相对温和的条件下，我们提供了理论结果，显示出因果因子和机制的可识别性。经验证明，该框架引导出高度解缠结的因果因子，提高了干预的稳健性，并且与因果生成相容。