标题： 纠缠缺失的图模型 显示英文标题

标题： Graphical Models of Entangled Missingness

摘要： 尽管在存在数据依赖性的场景中对因果和统计推断的兴趣不断增加，但目前很少有方法可以处理依赖数据设置中的缺失数据；统计学和因果推断中的大多数经典缺失数据方法都将数据单元视为独立同分布（i.i.d.）的。 我们开发了一个基于图形建模的框架，用于在纠缠缺失的情况下进行因果推断，定义为具有数据依赖性的缺失。 我们区分了三种可能发生的不同类型的纠缠，并通过现实世界的例子加以支持。 我们给出了所有三种情况下的有效且完整的识别结果。 我们表明，现有的缺失数据模型可以扩展以涵盖来自（1）目标定律依赖性和（2）缺失过程依赖性的纠缠，而来自（3）缺失干扰的纠缠则需要一种新方法。 我们在合成数据上展示了我们纠缠缺失框架的使用。 最后，我们讨论了如何在对模型中的变量进行某种重新解释的前提下，我们的缺失干扰模型将缺失数据方法扩展到i.i.d.设置中的新型缺失数据模式。 显示英文摘要

摘要： Despite the growing interest in causal and statistical inference for settings with data dependence, few methods currently exist to account for missing data in dependent data settings; most classical missing data methods in statistics and causal inference treat data units as independent and identically distributed (i.i.d.). We develop a graphical modeling based framework for causal inference in the presence of entangled missingness, defined as missingness with data dependence. We distinguish three different types of entanglements that can occur, supported by real-world examples. We give sound and complete identification results for all three settings. We show that existing missing data models may be extended to cover entanglements arising from (1) target law dependence and (2) missingness process dependence, while those arising from (3) missingness interference require a novel approach. We demonstrate the use of our entangled missingness framework on synthetic data. Finally, we discuss how, subject to a certain reinterpretation of the variables in the model, our model for missingness interference extends missing data methods to novel missing data patterns in i.i.d. settings.