aiqbench analysis
This research introduces UNIVERSE, a novel framework that addresses the Rashomon Effect by providing robust bounds on variable importance even when essential features are unobserved or omitted. By leveraging sets of near-optimal models, the method offers theoretical guarantees for feature selection in observational datasets where standard pipelines often fail due to missing variables. The approach demonstrates practical utility in high-stakes domains like credit risk, ensuring more reliable hypothesis generation and scientific validation in machine learning workflows.
Doctor Rashomon and the UNIVERSE of Madness: Variable Importance with Unobserved Confounding and the Rashomon Effect
arXiv:2510.12734v2 Announce Type: replace Abstract: Variable importance (VI) methods are often used for hypothesis generation, feature selection, and scientific validation. In the standard VI pipeline, an analyst estimates VI for a single predictive model with only the observed features. However, the importance of a feature depends heavily on which other variables are included in the model, and essential variables are often omitted from observational datasets. Moreover, the VI estimated for one model is often not the same as the VI estimated for another equally-good model - a phenomenon known as the Rashomon Effect. We address these gaps by introducing UNobservables and Inference for Variable importancE using Rashomon SEts (UNIVERSE). Our approach adapts Rashomon sets = the sets of near-optimal models in a dataset - to produce bounds on the true VI even with missing features. We theoretically guarantee the robustness of our approach, show strong performance on semi-synthetic simulations, and demonstrate its utility in a credit risk task.
Abstract:Variable importance (VI) methods are often used for hypothesis generation, feature selection, and scientific validation. In the standard VI pipeline, an analyst estimates VI for a single predictive model with only the observed features. However, the importance of a feature depends heavily on which other variables are included in the model, and essential variables are often omitted from observational datasets. Moreover, the VI estimated for one model is often not the same as the VI estimated for another equally-good model - a phenomenon known as the Rashomon Effect. We address these gaps by introducing UNobservables and Inference for Variable importancE using Rashomon SEts (UNIVERSE). Our approach adapts Rashomon sets = the sets of near-optimal models in a dataset - to produce bounds on the true VI even with missing features. We theoretically guarantee the robustness of our approach, show strong performance on semi-synthetic simulations, and demonstrate its utility in a credit risk task.
Machine Learning (cs.LG)
arXiv:2510.12734 [cs.LG]
(or arXiv:2510.12734v2 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2510.12734 Focus to learn more arXiv-issued DOI via DataCite
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