Brief

Researchers have introduced Disentangled Feature Importance (DFI), a new framework for attributing predictive signals from correlated variables. DFI maps covariates to an independent latent representation, computes importance in this latent space, and then attributes it back to the original features. This method is designed for post-hoc interpretation and provides stable, uncertainty-quantified attributions, distinguishing itself from conditional-incremental measures typically used for feature selection. AI