Research
Counterfactual Marginalisation: Framework for Evaluating Robustness to Nuisance Variables
arXiv:2609.10778v1 Announce Type: new Abstract: Machine learning models can achieve strong test performance while relying on demographic or acquisition-related shortcuts. We propose counterfactual (CF
arXiv:2609.10778v1 Announce Type: new 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.
Related
- Invariance Pair Guidance: Robustness to Spurious Correlations via Corrective Gradients
- Evaluating Time Series Foundation Models for Electricity Price Forecasting: Contamination Risk, Distributional Shifts, and Covariate Dependence
- Metamorphic Testing for Clinical ML Models: A Framework Proposal and Pilot Study
Source: arXiv cs.LG | 2026-09-11