Causal Representation Learning with Observational Grouping for CXR Classification
Journal:
arXiv
Published Date:
Jun 25, 2025
Abstract
Identifiable causal representation learning seeks to uncover the true causal
relationships underlying a data generation process. In medical imaging, this
presents opportunities to improve the generalisability and robustness of
task-specific latent features. This work introduces the concept of grouping
observations to learn identifiable representations for disease classification
in chest X-rays via an end-to-end framework. Our experiments demonstrate that
these causal representations improve generalisability and robustness across
multiple classification tasks when grouping is used to enforce invariance w.r.t
race, sex, and imaging views.
