Disentangled Feature Importance: A New Framework for Attributing Predictive Signals in Correlated Data
Researchers introduced Disentangled Feature Importance (DFI), a new statistical framework for attributing predictive signals when input variables are correlated, addressing a gap between feature selection and post-hoc interpretation goals. The method uses optimal transport geometry to map correlated variables to independent latent representations, then attributes importance back to original features through barycentric sensitivities. This approach is significant because it provides a principled way to interpret machine learning models when predictors are dependent, with theoretical guarantees and practical applications demonstrated in HIV-1 neutralization analysis.
The paper presents Disentangled Feature Importance (DFI), a population-level attribution framework designed to handle feature importance estimation when predictors are statistically dependent. Traditional conditional-incremental feature importance measures treat shared predictive information as redundancy, making them suitable for feature selection and compression but problematic for post-hoc model interpretation. DFI addresses this by mapping correlated covariates to an independent latent representation using entropic optimal transport, computing importance in the latent space, and attributing results back to original variables. The authors prove that DFI recovers classical R² decomposition for correlated regressors in the Gaussian linear case, provide influence-function-based inference with theoretical guarantees, and demonstrate stability and interpretability through simulations and an HIV-1 neutralization-resistance case study.
What's missing
The paper does not discuss computational complexity or scalability to high-dimensional settings, and does not compare runtime or practical feasibility against existing feature importance methods. Additionally, the conditions under which the method may fail or perform poorly relative to simpler alternatives are not explicitly detailed.
What different sources said
- arXiv cs.LGCenter
Disentangled Feature Importance
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