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Counterfactual Marginalisation: Framework for Evaluating Robustness to Nuisance Variables

arXiv · AI, language, vision and robotics · article · Sep 9, 2026 · UTC

Machine learning models can achieve strong test performance while relying on demographic or acquisition-related shortcuts. We propose counterfactual (CF) marginalisation as a test-time evaluation procedure for assessing robustness of classification models to such variables. Given a CF image generator, we intervene on nuisance parent variables such as age or sex, generate CF versions of each test image, and average predictions over a target intervention distribution. This produces intervention-aware predictions that marginalise demographic effects while preserving patient-specific latent inform

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