{"id":5031,"date":"2025-07-02T07:02:44","date_gmt":"2025-07-02T07:02:44","guid":{"rendered":"https:\/\/mailitics.com\/index.php\/2025\/07\/02\/2507-00260\/"},"modified":"2025-07-02T07:02:44","modified_gmt":"2025-07-02T07:02:44","slug":"2507-00260","status":"publish","type":"post","link":"https:\/\/mailitics.com\/index.php\/2025\/07\/02\/2507-00260\/","title":{"rendered":"Disentangled Feature Importance"},"content":{"rendered":"

Disentangled Feature Importance
\n \t

\n
<\/BR>
\n
\n \t

\n
<\/BR><\/p>\n

arXiv:2507.00260v1 Announce Type: new
\nAbstract: Feature importance quantification faces a fundamental challenge: when predictors are correlated, standard methods systematically underestimate their contributions. We prove that major existing approaches target identical population functionals under squared-error loss, revealing why they share this correlation-induced bias.
\n To address this limitation, we introduce emph{Disentangled Feature Importance (DFI)}, a nonparametric generalization of the classical $R^2$ decomposition via optimal transport. DFI transforms correlated features into independent latent variables using a transport map, eliminating correlation distortion. Importance is computed in this disentangled space and attributed back through the transport map’s sensitivity. DFI provides a principled decomposition of importance scores that sum to the total predictive variability for latent additive models and to interaction-weighted functional ANOVA variances more generally, under arbitrary feature dependencies.
\n We develop a comprehensive semiparametric theory for DFI. For general transport maps, we establish root-$n$ consistency and asymptotic normality of importance estimators in the latent space, which extends to the original feature space for the Bures-Wasserstein map. Notably, our estimators achieve second-order estimation error, which vanishes if both regression function and transport map estimation errors are $o_{mathbb{P}}(n^{-1\/4})$. By design, DFI avoids the computational burden of repeated submodel refitting and the challenges of conditional covariate distribution estimation, thereby achieving computational efficiency.<\/div>\n

\t

\n
<\/BR>
\n Jin-Hong Du, Kathryn Roeder, Larry Wasserman
\n \t

\n
<\/BR>
\nGo to original source<\/a>
\n \t

\n
<\/BR><\/p>\n","protected":false},"excerpt":{"rendered":"

Disentangled Feature Importance arXiv:2507.00260v1 Announce Type: new Abstract: Feature importance quantification faces a fundamental challenge: when predictors are correlated, standard methods systematically underestimate their contributions. We prove that major existing approaches target identical population functionals under squared-error loss, revealing why they share this correlation-induced bias. To address this limitation, we introduce emph{Disentangled Feature Importance (DFI)}, […]<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[62,113,190,183,112,191],"tags":[3126,321,1524],"class_list":["post-5031","post","type-post","status-publish","format-standard","hentry","category-aimldsaimlds","category-cs-lg","category-math-st","category-stat-me","category-stat-ml","category-stat-th","tag-dfi","tag-feature","tag-importance"],"_links":{"self":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts\/5031"}],"collection":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/comments?post=5031"}],"version-history":[{"count":0,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts\/5031\/revisions"}],"wp:attachment":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/media?parent=5031"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/categories?post=5031"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/tags?post=5031"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}