论文标题
缺少数据的图形模型中的完整法律标识：完整性结果
Full Law Identification In Graphical Models Of Missing Data: Completeness Results
论文作者
论文摘要
缺少的数据有可能影响在科学研究的所有领域进行的分析，包括医疗保健，经济学和社会科学。在存在不可忽视的缺失的情况下，几种无偏见的方法取决于目标分布的规范及其丢失过程作为概率分布，该概率分布相对于有向的无环形图。在本文中，我们解决了在此类缺失数据分布中可识别的模型表征的长期问题。我们提供了该研究领域的第一个完整性结果 - 必要和足够的图形条件，可以从观察到的数据分布中恢复完整的数据分布。然后，我们通过将这些图形条件和完整性的证据扩展到某些变量不仅丢失，而且完全没有观察到的设置，同时解决由于缺少数据和未衡量的混杂而可能出现的问题。
Missing data has the potential to affect analyses conducted in all fields of scientific study, including healthcare, economics, and the social sciences. Several approaches to unbiased inference in the presence of non-ignorable missingness rely on the specification of the target distribution and its missingness process as a probability distribution that factorizes with respect to a directed acyclic graph. In this paper, we address the longstanding question of the characterization of models that are identifiable within this class of missing data distributions. We provide the first completeness result in this field of study -- necessary and sufficient graphical conditions under which, the full data distribution can be recovered from the observed data distribution. We then simultaneously address issues that may arise due to the presence of both missing data and unmeasured confounding, by extending these graphical conditions and proofs of completeness, to settings where some variables are not just missing, but completely unobserved.
