Causal Inference with Unmeasured Confounding from Nonignorable Missing Outcomes
不可忽略结局缺失与未测混杂下的因果推断AI 解读 ↓
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不可忽略结局缺失与未测混杂下的因果推断
- 研究问题
- 研究在未测混杂和不可忽略的结局缺失同时存在时，如何恢复完整数据的信息，并识别和估计服从者平均因果效应。
- 方法与思路
- 作者引入兼具影子变量和工具变量角色的辅助变量，提出完整数据分布的非参数识别条件，再用广义矩估计恢复均值并调整混杂。
- 材料陈述的贡献
- 摘要称，论文提供辅助变量下的识别框架、服从者效应估计与参数渐近结果，并通过模拟及政治分析数据应用展示方法。
- 阅读关注点
- 应核对影子变量与工具变量要求能否同时成立、完整数据识别条件，以及从恢复结局均值到服从者效应估计的各步推导。
- 阅读边界
- 所据版本为 arXiv 预印本，同行评审与期刊接收未核实；摘要未列出辅助变量的精确约束、识别条件或应用估计数值。
原文摘要
Observational studies are the primary source of data for causal inference, but it is challenging when existing unmeasured confounding. Missing data problems are also common in observational studies. How to obtain the causal effects from the nonignorable missing data with unmeasured confounding is a challenge. In this paper, we consider that how to obtain complier average causal effect with unmeasured confounding from the nonignorable missing outcomes. We propose an auxiliary variable which plays two roles simultaneously, the one is the shadow variable for identification and the other is the instrumental variable for inference. We also illustrate some difference between some missing outcomes mechanisms in the previous work and the shadow variable assumption. We give a causal diagram to illustrate this description. Under such a setting, we present a general condition for nonparametric identification of the full data law from the nonignorable missing outcomes with this auxiliary variable. For inference, firstly, we recover the mean value of the outcome based on the generalized method of moments. Secondly, we propose an estimator to adjust for the unmeasured confounding to obtain complier average causal effect. We also establish the asymptotic results of the estimated parameters. We evaluate its performance via simulations and apply it to a real-life dataset about a political analysis.