Causal Representation Learning with Observational Grouping for CXR Classification

Rajat Rasal; Avinash Kori; Ben Glocker

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arXiv · 2506.20582

Causal Representation Learning with Observational Grouping for CXR Classification

Rajat Rasal·Avinash Kori·Ben Glocker

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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.

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BibTeX RIS

Rajat Rasal, Avinash Kori, Ben Glocker. 2025-06-25. Causal Representation Learning with Observational Grouping for CXR Classification. https://arxiv.org/abs/2506.20582

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