Causal Inference: A Missing Data Perspective
Causal Inference: A Missing Data Perspective
复制标题
DOI：
10.1214/18-sts645
复制
发表时间：
2018-05-01
影响因子：
5.7
通讯作者：
Li, Fan
中科院分区：
文献类型：
--
作者：
Ding, Peng;Li, Fan
推断治疗的因果效应是许多学科的中心目标。潜在结果框架是因果推理的主要统计方法，其中因果效应被定义为相同单元在不同处理条件下的潜在结果的比较。因为对于每个单元，最多只能观察到一个潜在结果，而其余的都缺失了，所以因果推理本质上是一个缺失数据的问题。的确，因果推理和缺失数据之间的术语和推理框架有密切的相似之处。因果推理和缺失数据的统计分析虽然具有内在的联系，但在目的、背景和方法上也有显著的差异。本文从缺失数据的角度对因果推理进行了系统的回顾。针对可忽略处理分配机制，我们讨论了一系列与缺失数据分析类似的因果推理方法，如归算、逆概率加权和双鲁棒方法。在三种推理模式——频率随机、贝叶斯随机和费希尔随机——下，我们分别给出了有限样本和超总体估计的一般推理结构，并通过具体的例子加以说明。我们确定开放的问题，以激发更多的研究，以弥合这两个领域。
Inferring causal effects of treatments is a central goal in many disciplines. The potential outcomes framework is a main statistical approach to causal inference, in which a causal effect is defined as a comparison of the potential outcomes of the same units under different treatment conditions. Because for each unit at most one of the potential outcomes is observed and the rest are missing, causal inference is inherently a missing data problem. Indeed, there is a close analogy in the terminology and the inferential framework between causal inference and missing data. Despite the intrinsic connection between the two subjects, statistical analyses of causal inference and missing data also have marked differences in aims, settings and methods. This article provides a systematic review of causal inference from the missing data perspective. Focusing on ignorable treatment assignment mechanisms, we discuss a wide range of causal inference methods that have analogues in missing data analysis, such as imputation, inverse probability weighting and doubly robust methods. Under each of the three modes of inference-Frequentist, Bayesian and Fisherian randomization-we present the general structure of inference for both finite-sample and super-population estimands, and illustrate via specific examples. We identify open questions to motivate more research to bridge the two fields.