The Attribution Impossibility: No Feature Ranking Is Faithful, Stable, and Complete Under Collinearity

The paper proves that no feature ranking can be simultaneously faithful, stable, and complete when features are collinear, as ranking for collinear pairs reduces to a coin flip. It resolves this impossibility through ensemble averaging (DASH), characterizing the complete attribution design space and quantifying the problem for different model classes.