Identification and estimation of causal peer effects using double negative controls for unmeasured network confounding.
Identification and estimation of causal peer effects using double negative controls for unmeasured network confounding.
复制标题
使用双负控制来识别和估计因果同伴效应，以防止无法测量的网络混杂。
DOI：
10.1093/jrsssb/qkad132
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发表时间：
2024
期刊：
影响因子：
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通讯作者：
TchetgenTchetgen,EricJ
中科院分区：
文献类型：
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作者：
Egami,Naoki;TchetgenTchetgen,EricJ
在观察性研究中，识别和估计因果同伴效应具有挑战性，原因有两个。第一个是由于无法测量的网络混杂（例如同质性偏差和上下文混杂）造成的识别挑战。第二个是观察的网络依赖性。我们建立了一个框架，利用一对负控制结果和暴露变量（双负控制）来非参数地识别存在不可测量的网络混杂的因果同伴效应。然后，我们提出了一种广义的矩估计方法，并在ψ网络依赖性的假设下建立了其一致性和渐近正态性。最后，我们提供了一致的方差估计器。
Identification and estimation of causal peer effects are challenging in observational studies for two reasons. The first is the identification challenge due to unmeasured network confounding, for example, homophily bias and contextual confounding. The second is network dependence of observations. We establish a framework that leverages a pair of negative control outcome and exposure variables (double negative controls) to non-parametrically identify causal peer effects in the presence of unmeasured network confounding. We then propose a generalised method of moments estimator and establish its consistency and asymptotic normality under an assumption aboutψ-network dependence. Finally, we provide a consistent variance estimator.