A Confounding Bridge Approach for Double Negative Control Inference on Causal Effects (Supplement and Sample Codes are included)
A Confounding Bridge Approach for Double Negative Control Inference on Causal Effects (Supplement and Sample Codes are included)
复制标题
因果效应双阴性对照推断的混杂桥方法（包括补充和示例代码）
DOI：
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发表时间：
2018
期刊：
影响因子：
--
通讯作者：
E. Tchetgen
中科院分区：
文献类型：
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作者：
Wang Miao;E. Tchetgen
不可测混杂是因果推理的一个关键挑战。阴性对照变量在观察性研究中广泛可用。阴性对照结果与混杂因素相关，但不受所考虑的暴露的因果影响，阴性对照暴露与主要暴露或混杂因素相关，但不对关注结果产生因果影响。在本文中，我们建立了一个框架，使用它们的不可测量的混杂调整。我们引入了一个混淆的桥梁功能，连接潜在的结果的意思和阴性对照结果的分布，我们将一个阴性对照暴露，以确定桥梁功能和平均因果关系。我们的方法可以用来修复一个无效的工具变量的情况下，它与不可测量的混杂因素。我们还扩展了我们的方法，允许之间的因果关系的主要曝光和控制结果。我们用模拟来说明我们的方法，并将其应用于空气污染的短期影响的研究。虽然标准分析显示PM2.5对死亡率有显著的急性影响，但我们的分析表明，这种影响可能是混淆的，在双阴性对照调整后，这种影响逐渐减弱。
Unmeasured confounding is a key challenge for causal inference. Negative control variables are widely available in observational studies. A negative control outcome is associated with the confounder but not causally affected by the exposure in view, and a negative control exposure is correlated with the primary exposure or the confounder but does not causally affect the outcome of interest. In this paper, we establish a framework to use them for unmeasured confounding adjustment. We introduce a confounding bridge function that links the potential outcome mean and the negative control outcome distribution, and we incorporate a negative control exposure to identify the bridge function and the average causal effect. Our approach can be used to repair an invalid instrumental variable in case it is correlated with the unmeasured confounder. We also extend our approach by allowing for a causal association between the primary exposure and the control outcome. We illustrate our approach with simulations and apply it to a study about the short-term effect of air pollution. Although a standard analysis shows a significant acute effect of PM2.5 on mortality, our analysis indicates that this effect may be confounded, and after double negative control adjustment, the effect is attenuated toward zero.
用未衡量的混杂因素的代理变量识别因果效应。
影响因子：
2.7
作者：
Miao W;Geng Z;Tchetgen Tchetgen E
通讯作者：
Tchetgen Tchetgen E
DOI：
10.1214/09-sts313
发表时间：
2010-02-01
期刊：
Statistical science : a review journal of the Institute of Mathematical Statistics
影响因子：
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作者：
Stuart EA
通讯作者：
Stuart EA
DOI：
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发表时间：
2017
期刊：
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影响因子：
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作者：
Wang Miao;E. T. Tchetgen
通讯作者：
Wang Miao;E. T. Tchetgen