Diffusion Based Causal Representation Learning
Amir Mohammad Karimi Mamaghan Andrea Dittadi Stefan Bauer Karl Henrik Johansson Francesco Quinzan
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.