What is counterfactual fairness in model evaluation?

Counterfactual fairness ensures predictions remain unchanged under sensitive attribute changes.

A model is counterfactually fair if its predictions do not change when sensitive attributes (like gender or race) are altered in a hypothetical world while keeping all other factors constant. It relies on structural causal models and is widely used in fairness-aware ML systems.