Counterfactual Marginalisation: Framework for Evaluating Robustness to Nuisance Variables
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Computer Science > Machine Learning
Title:Counterfactual Marginalisation: Framework for Evaluating Robustness to Nuisance Variables
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.
| Comments: | Accepted at UNSURE Workshop, MICCAI 2026 |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2609.10778 [cs.LG] |
| (or arXiv:2609.10778v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2609.10778
arXiv-issued DOI via DataCite (pending registration)
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