A Causal Ordering Prior for Unsupervised Representation Learning

• URL: http://arxiv.org/abs/2307.05704v1
• Date: Tue, 11 Jul 2023 18:12:05 GMT
• Title: A Causal Ordering Prior for Unsupervised Representation Learning
• Authors: Avinash Kori, Pedro Sanchez, Konstantinos Vilouras, Ben Glocker, Sotirios A. Tsaftaris
• Abstract summary: Causal representation learning argues that factors of variation in a dataset are, in fact, causally related. We propose a fully unsupervised representation learning method that considers a data generation process with a latent additive noise model.
• Score: 27.18951912984905
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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