A Causal Ordering Prior for Unsupervised Representation Learning
Primary Area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
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Keywords: latent causal ordering, ANM, identifiability results, representation learning
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TL;DR: We propose a causal representation learning method without any auxiliary data by promoting a causal ordering in the latent space, resulting in an improved identifiability class of the models.
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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Submission Number: 6924
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