Title: Flow-Disentangled Feature Importance
Primary area: interpretability and explainable AI
Keywords: Interpretability, Feature Importance, Statistical Inference, Correlation Distortion, Uncertainty Quantification

Abstract: Reliably quantifying both feature importance and its statistical uncertainty is a fundamental goal for interpretability, yet classical model-agnostic methods produce unreliable attributions that compromise statistical validity when features are correlated. Statistical approaches that address correlation through feature decorrelation have shown promise but remain restricted to $\ell_2$ loss, limiting their applicability across diverse machine learning tasks. We introduce Flow-Disentangled Feature Importance (FDFI), a model-agnostic framework that resolves these limitations by combining principled statistical inference with computational flexibility. FDFI leverages flow matching to learn flexible disentanglement maps that not only handle arbitrary feature distributions but also provide an interpretable pathway for understanding how importance is attributed through the data's correlation structure. The framework generalizes the decorrelation-based attribution to general differentiable loss functions, enabling statistically valid importance assessment for black-box predictors across regression and classification. We establish statistical inference theory, deriving semiparametric efficiency of FDFI estimators, which enables valid confidence intervals and hypothesis testing with Type I error control. Experiments demonstrate that FDFI achieves substantially higher statistical power than existing removal-based and conditional permutation approaches while maintaining robust, interpretable attributions under severe multicollinearity on synthetic and biomedical datasets.