Unsupervised representation learning with variational inference relies heavily on independence assumptions over latent variables. Causal representation learning (CRL), however, argues that factors of variation in a dataset are, in fact, causally related. Allowing latent variables to be correlated, as a consequence of causal relationships, is more realistic and generalisable. So far, provably identifiable methods rely on: auxiliary information, weak labels, and interventional or even counterfactual data. Inspired by causal discovery with functional causal models, we propose a fully unsupervised representation learning method that considers a data generation process with a latent additive noise model (ANM). We encourage the latent space to follow a causal ordering via loss function based on the Hessian of the latent distribution.
@article{arxiv.2307.05704,
title = {A Causal Ordering Prior for Unsupervised Representation Learning},
author = {Avinash Kori and Pedro Sanchez and Konstantinos Vilouras and Ben Glocker and Sotirios A. Tsaftaris},
journal= {arXiv preprint arXiv:2307.05704},
year = {2023}
}