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This paper introduces "counterfactual marginalisation," a test-time evaluation framework designed to assess machine learning models' robustness against nuisance variables like age or sex. By generating counterfactual versions of input data, the method isolates demographic shortcuts while preserving essential patient-specific latent information for more reliable risk metrics. This approach directly addresses critical issues in model fairness and generalizability within classification tasks.
Counterfactual Marginalisation: Framework for Evaluating Robustness to Nuisance Variables
arXiv:2609.10778v1 Announce Type: cross 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.
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.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
arXiv:2609.10778 [cs.LG]
(or arXiv:2609.10778v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2609.10778 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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