Causal reasoning can be considered a cornerstone of intelligent systems. Having access to an underlying causal graph comes with the promise of cause-effect estimation and the identification of efficient and safe interventions. However, learning causal representations remains a major challenge, due to the complexity of many real-world systems. Previous works on causal representation learning have mostly focused on Variational Auto-Encoders (VAE). These methods only provide representations from a point estimate, and they are unsuitable to handle high dimensions. To overcome these problems, we proposed a new Diffusion-based Causal Representation Learning (DCRL) algorithm. This algorithm uses diffusion-based representations for causal discovery. DCRL offers access to infinite dimensional latent codes, which encode different levels of information in the latent code. In a first proof of principle, we investigate the use of DCRL for causal representation learning. We further demonstrate experimentally that this approach performs comparably well in identifying the causal structure and causal variables.

翻译：因果推理可视为智能系统的基石。获取底层因果图可支持因果效应估计及高效安全干预措施的识别。然而，由于现实世界系统的复杂性，因果表示学习仍面临重大挑战。现有因果表示学习研究多聚焦于变分自编码器，这些方法仅能提供点估计表示，且难以处理高维数据。为解决上述问题，我们提出了一种新的基于扩散的因果表示学习算法。该算法利用扩散模型生成的表示进行因果发现，可提供编码不同层级信息的无限维潜在编码。在首次原理验证中，我们探究了DCRL在因果表示学习中的应用。实验进一步证明，该方法在识别因果结构与因果变量方面具有与现有方法相当的性能。