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Handling Missing Data in Instrumental Variable Methods for Causal Inference.
Annual Review of Statistics and Its Application ( IF 7.4 ) Pub Date : 2018-11-28 , DOI: 10.1146/annurev-statistics-031017-100353
Edward H Kennedy 1 , Jacqueline A Mauro 1 , Michael J Daniels 2 , Natalie Burns 2 , Dylan S Small 3
Affiliation  

It is very common in instrumental variable studies for there to be missing instrument data. For example, in the Wisconsin Longitudinal Study one can use genotype data as a Mendelian randomization-style instrument, but this information is often missing when subjects do not contribute saliva samples, or when the genotyping platform output is ambiguous. Here we review missing-at-random assumptions one can use to identify instrumental variable causal effects, and discuss various approaches for estimation and inference. We consider likelihood-based methods, regression and weighting estimators, and doubly robust estimators. The likelihood-based methods yield the most precise inference, and are optimal under the model assumptions, while the doubly robust estimators can attain the nonparametric efficiency bound while allowing flexible nonparametric estimation of nuisance functions (e.g., instrument propensity scores). The regression and weighting estimators can sometimes be easiest to describe and implement. Our main contribution is an extensive review of this wide array of estimators under varied missing-at-random assumptions, along with discussion of asymptotic properties and inferential tools. We also implement many of the estimators in an analysis of the Wisconsin Longitudinal Study, to study effects of impaired cognitive functioning on depression.

中文翻译:

在因果推断的工具变量方法中处理缺失数据。

在仪器变量研究中很常见的是缺少仪器数据。例如,在威斯康星州纵向研究中,人们可以将基因型数据用作孟德尔随机化样式的仪器,但是当受试者没有贡献唾液样本或基因分型平台输出不明确时,通常会缺少此信息。在这里,我们回顾了可以用于识别工具变量因果效应的随机缺失假设,并讨论了各种估算和推断方法。我们考虑基于似然的方法,回归和加权估计器以及双重稳健估计器。基于似然的方法可以得出最精确的推论,并且在模型假设下是最优的,而双重鲁棒估计量可以达到非参数效率范围,同时允许对扰动函数(例如,仪器倾向评分)进行灵活的非参数估计。回归和加权估算器有时可能最容易描述和实施。我们的主要贡献是在各种随机缺失假设下对大量估计量进行了广泛的综述,并讨论了渐近性质和推论工具。在威斯康星州纵向研究的分析中,我们还采用了许多估计量,以研究认知功能受损对抑郁的影响。我们的主要贡献是在各种随机缺失假设下对大量估计量进行了广泛的综述,并讨论了渐近性质和推论工具。在威斯康星州纵向研究的分析中,我们还采用了许多估计量,以研究认知功能受损对抑郁的影响。我们的主要贡献是在各种随机缺失假设下对大量估计量进行了广泛的综述,并讨论了渐近性质和推论工具。在威斯康星州纵向研究的分析中,我们还采用了许多估计量,以研究认知功能受损对抑郁的影响。
更新日期:2018-11-28
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