提出新方法在缺失变量下仍能可靠估计特征重要性。
Doctor Rashomon and the UNIVERSE of Madness: Variable Importance with Unobserved Confounding and the Rashomon Effect
- 用近优模型集合构建特征重要性边界，应对未观测混杂。
- 在模拟和信贷风险任务中验证了方法的稳健性与实用性。
- 适合做因果推断或特征选择时担心遗漏变量的研究者。
特征重要性（VI）常用于假设生成、特征选择和科学验证。标准流程仅基于可观测特征估计单一模型的VI，但特征重要性高度依赖于模型中包含的其他变量，且关键变量常被遗漏。此外，表现相当的不同模型会给出不同的VI结果，即“Rashomon效应”。本文提出UNIVERSE方法，通过利用近优模型集合（Rashomon sets）在存在未观测混杂时提供真实的特征重要性范围。理论证明该方法具有鲁棒性，在半合成数据上表现优异，并在信用风险任务中展示了实际价值。
原文摘要 · Abstract (English)
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.
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