Graphical Models for Processing Missing Data
成果类型：
Review
署名作者：
Mohan, Karthika; Pearl, Judea
署名单位：
University of California System; University of California Berkeley; University of California System; University of California Los Angeles
刊物名称：
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
ISSN/ISSBN：
0162-1459
DOI：
10.1080/01621459.2021.1874961
发表日期：
2021
页码：
1023-1037
关键词：
摘要：
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.