更多空值阴影：面向负责任的缺失值填补的评估套件
Still More Shades of Null: An Evaluation Suite for Responsible Missing Value Imputation
AI总结：
本文提出 Shades-of-Null 评估套件，建模多机制缺失和缺失偏移等现实场景，从填补质量、公平性及下游模型性能等维度全面评估填补方法，并通过大规模实验揭示不同场景下的权衡。
摘要：
数据缺失是科学界持续关注的一个实际挑战。在本文中，我们提出了 Shades-of-Null，一个用于负责任缺失值填补的评估套件。我们的工作有两个新颖之处：（i）我们建模了现实且具有社会显著性的缺失场景，超越了 Rubin 经典的完全随机缺失（MCAR）、随机缺失（MAR）和非随机缺失（MNAR）设置，纳入了多机制缺失（当数据中同时存在不同的缺失模式时）和缺失偏移（当训练集和测试集之间的缺失机制发生变化时）；（ii）我们基于填补质量和填补公平性，以及在填补后数据上训练和测试的模型的预测性能、公平性和稳定性，对填补器进行全面评估。我们使用 Shades-of-Null 进行了一项涉及 29,736 条实验流水线的大规模实证研究，发现虽然没有单一的最佳填补方法适用于所有缺失类型，但根据缺失场景、填补器选择和预测模型架构的组合，预测性能、公平性和稳定性之间会出现有趣的权衡。我们将 Shades-of-Null 公开发布，使研究人员能够在合理且具有社会意义的场景中，基于广泛的指标严格评估缺失值填补方法。
英文摘要：
Data missingness is a practical challenge of sustained interest to the scientific community. In this paper, we present Shades-of-Null, an evaluation suite for responsible missing value imputation. Our work is novel in two ways (i) we model realistic and socially-salient missingness scenarios that go beyond Rubin's classic Missing Completely at Random (MCAR), Missing At Random (MAR) and Missing Not At Random (MNAR) settings, to include multi-mechanism missingness (when different missingness patterns co-exist in the data) and missingness shift (when the missingness mechanism changes between training and test) (ii) we evaluate imputers holistically, based on imputation quality and imputation fairness, as well as on the predictive performance, fairness and stability of the models that are trained and tested on the data post-imputation. We use Shades-of-Null to conduct a large-scale empirical study involving 29,736 experimental pipelines, and find that while there is no single best-performing imputation approach for all missingness types, interesting trade-offs arise between predictive performance, fairness and stability, based on the combination of missingness scenario, imputer choice, and the architecture of the predictive model. We make Shades-of-Null publicly available, to enable researchers to rigorously evaluate missing value imputation methods on a wide range of metrics in plausible and socially meaningful scenarios.
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