Toward a more transparent causal representation learning

Mouad El Bouchattaoui, Myriam Tami, BENOIT LEPETIT, Paul-Henry Cournède

Keywords: Causal Representation Learning, Sparsity, Clustering

Abstract: This work addresses the challenge of causal representation learning (CRL) for complex, high-dimensional, time-varying data. We enhance transparency and confidence in learned causal abstractions by linking them to observational space. The existing literature rarely explores the association between latent causal variables and observed ones, with only one notable work imposing a simplistic single-latent-factor decoding constraint. Our approach, in contrast, allows for a flexible entangling of latent factors, reflecting the complexity of real-world datasets. We introduce a structural sparsity pattern over generative functions and leverage induced grouping structures over observed variables for better model understanding. Our regularization technique, based on sparse subspace clustering over the Jacobian matrix of the decoder, promotes the sparsity and readability of model results. We apply our model to real-world datasets, including Saint-Gobain purchase data and MIMIC III medical data.

Submission Number: 25