标题： 缺失数据下的随机森林置换重要性的置信区间 显示英文标题

标题： Confidence Intervals for Random Forest Permutation Importance with Missing Data

摘要： 随机森林以其预测准确性而闻名，但关于基于排列的特征重要性的有效推断仍然具有挑战性。 现有的方法，例如Ishwaran等人（2019）提出的置信区间（CIs），是有希望的，但假设所有特征都完全观测到。 然而，现实世界的数据通常包含缺失值。 在本文中，我们研究了常见的插补技术在数据不完整时对随机森林排列重要性置信区间有效性的影响。 通过广泛的模拟和真实世界基准研究，我们在各种缺失数据机制和缺失率下比较了最先进的插补方法。 我们的结果表明，单次插补策略导致置信区间覆盖度较低。 作为解决方法，我们将Rubin规则适应于在多个插补数据集中汇总特征重要性估计及其方差，并考虑插补不确定性。 我们的数值结果表明，调整后的置信区间实现了更好的名义覆盖度。 显示英文摘要

摘要： Random Forests are renowned for their predictive accuracy, but valid inference, particularly about permutation-based feature importances, remains challenging. Existing methods, such as the confidence intervals (CIs) from Ishwaran et al. (2019), are promising but assume complete feature observation. However, real-world data often contains missing values. In this paper, we investigate how common imputation techniques affect the validity of Random Forest permutation-importance CIs when data are incomplete. Through an extensive simulation and real-world benchmark study, we compare state-of-the-art imputation methods across various missing-data mechanisms and missing rates. Our results show that single-imputation strategies lead to low CI coverage. As a remedy, we adapt Rubin's rule to aggregate feature-importance estimates and their variances over several imputed datasets and account for imputation uncertainty. Our numerical results indicate that the adjusted CIs achieve better nominal coverage.