NO UNMEASURED CONFOUNDING: KNOWN UNKNOWNS OR… NOT?
NO UNMEASURED CONFOUNDING: KNOWN UNKNOWNS OR… NOT?
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没有未测量的混杂因素：是已知还是未知？
DOI：
10.1093/aje/kwad133
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发表时间：
2023
影响因子：
5
通讯作者：
Shortreed,SusanM
中科院分区：
文献类型：
--
作者：
Schulz,Juliana;Moodie,EricaEM;Shortreed,SusanM
因果关系文献中描述的大多数方法依赖于无不可测量混杂（NUC）的假设。在估计治疗效应时，NUC假设要求测量与治疗暴露和关注结局相关的所有变量。在这封简短的信中，我们讨论了两种可能认为可以验证NUC假设的方法，并表明这两种方法都不适合于此目的。最后，我们提醒读者对数据复杂性的乐观看法，以及观察到的变量和未测量的变量之间的相关性如何减少与未测量信息相关的任何偏差。我们从一些符号开始开始。假设我们有兴趣用基于回归的框架来估计平均治疗效果。令Y表示连续结果，X表示测量的混杂因素（可能是向量），U表示未测量的混杂因素，A表示二元治疗。我们假设结构模型
A large majority of methods described in the causal literature rely on the assumption of no unmeasured confounding (NUC). When estimating treatment effects, the NUC assumption requires the measurement of all variables related to both treatment exposure and the outcome (s) of interest. In this short letter, we discuss 2 approaches which one might think could provide validation of the NUC assumption and show that neither is appropriate for this purpose. We close with a reminder to readers of the optimistic view of the complexity of data and how correlation between observed variables and unmeasured ones can reduce any bias associated with unmeasured information. We begin with some notation. Suppose we are interested in estimating an average treatment effect with a regressionbased framework. Let Y denote a continuous outcome, X a measured confounder (possibly a vector), U an unmeasured confounder, and A a binary treatment. We assume the structural model