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arXiv · 2609.10778

Counterfactual Marginalisation: Framework for Evaluating Robustness to Nuisance Variables

Yasin Ibrahim·Hermione Warr·Robin J. Evans·Konstantinos Kamnitsas
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Abstract

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 information. We use these predictions to define metrics for CF risk, calibration, stability and worst-case sensitivity. We demonstrate this framework's utility for quantitative robustness evaluation.

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Yasin Ibrahim, Hermione Warr, Robin J. Evans, Konstantinos Kamnitsas. 2026-09-09. Counterfactual Marginalisation: Framework for Evaluating Robustness to Nuisance Variables. https://arxiv.org/abs/2609.10778

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