Significant progress has been made in developing identification and estimation techniques for missing data problems where modeling assumptions can be described via a directed acyclic graph. The validity of results using such techniques rely on the assumptions encoded by the graph holding true; however, verification of these assumptions has not received sufficient attention in prior work. In this paper, we provide new insights on the testable implications of three broad classes of missing data graphical models, and design goodness-of-fit tests for them. The classes of models explored are: sequential missing-at-random and missing-not-at-random models which can be used for modeling longitudinal studies with dropout/censoring, and a no self-censoring model which can be applied to cross-sectional studies and surveys.

翻译：在开发针对缺失数据问题的识别与估计技术方面已取得显著进展，其中建模假设可通过有向无环图描述。此类技术结果的有效性依赖于图所编码的假设成立；然而，这些假设的验证在以往研究中尚未得到充分关注。本文为三类广泛缺失数据图模型的可检验蕴含关系提供了新见解，并设计了相应的拟合优度检验方法。探讨的模型类别包括：可用于含删失/截尾纵向研究的序列随机缺失与非随机缺失模型，以及适用于横断面研究与调查的无自删失模型。