Abstract

This article reviews recent advances in missing data research using graphical models to represent multivariate dependencies. We first examine the limitations of traditional frameworks from three different perspectives: transparency, estimability, and testability. We then show how procedures based on graphical models can overcome these limitations and provide meaningful performance guarantees even when data are missing not at random (MNAR). In particular, we identify conditions that guarantee consistent estimation in broad categories of missing data problems, and derive procedures for implementing this estimation. Finally, we derive testable implications for missing data models in both missing at random and MNAR categories.

摘要

本文回顾了使用图形模型来表示多元依赖关系的缺失数据研究的最新进展。我们首先从三个不同的角度检查传统框架的局限性：透明度、可估计性和可测试性。然后，我们展示了基于图形模型的程序如何克服这些限制并提供有意义的性能保证，即使数据不是随机丢失 (MNAR)。特别是，我们确定了在广泛的缺失数据问题类别中保证一致估计的条件，并推导出实施该估计的程序。最后，我们推导出随机缺失和 MNAR 类别中缺失数据模型的可测试含义。