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Arxiv
1d
185
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Disentangled Feature Importance
- Standard methods for feature importance quantification underestimate contributions when predictors are correlated.
- Introduction of Disentangled Feature Importance (DFI) aims to address this limitation by transforming correlated features into independent latent variables using a transport map.
- DFI provides a principled decomposition of importance scores that sum to the total predictive variability for latent additive models and interaction-weighted functional ANOVA variances under arbitrary feature dependencies.
- Comprehensive semiparametric theory for DFI establishes root-n consistency and asymptotic normality of importance estimators in the latent space, achieving computational efficiency.
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