Counterfactual Marginalisation: Framework for Evaluating Robustness to Nuisance Variables
Quick summary
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 effec
Key takeaways
- 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.
Why it matters
“Counterfactual Marginalisation: Framework for Evaluating Robustness to Nuisance Variables” highlights the need for repeatable measurement rather than a single impressive demonstration. Independent validation across datasets and clearly stated limitations determine whether a result can guide product decisions.
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