# Non-factorised identifiable variational autoencoders for causal discovery and out-of-distribution generalisation

Abstract: “In many situations, given a set of observations, we would like to find the factors which cause or influence the data we observe. To learn these factors, we can use a deep generative model such as a variational auto-encoder (VAE), which maps the data to a latent space. However, a limitation of the VAE is that it is not identifiable, in the sense that two different sets of parameters may yield the same model. To address this, it is possible to augment the VAE in such a way that it becomes identifiable while remaining capable of flexible representations. This paper explores invariant causal representation learning (ICaRL), an algorithm for causal representation learning with possible applications in causal discovery and out-of-distribution generalisation.”