Principles of confounder selection
Principles of confounder selection
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DOI：
10.1007/s10654-019-00494-6
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发表时间：
2019-03-01
影响因子：
13.6
通讯作者：
VanderWeele, Tyler J.
中科院分区：
文献类型：
--
作者：
VanderWeele, Tyler J.
选择一组合适的混杂因素进行控制是可靠的因果推理的关键。最近的理论和方法的发展有助于澄清一些原则的混杂选择。当所有协变量相互关联的因果关系图的完整知识可用时，图形规则可以用于做出关于协变量控制的决策。不幸的是，这种完整的知识往往是不可用的。本文提出了一种实用的方法来混淆选择决策时，thesomewhat不太严格的假设，知识是可用于每个协变量是否是一个原因的曝光，以及它是否是一个原因的结果。基于因果推理文献中最近理论上合理的发展，提出了以下协变量控制决策建议：控制作为暴露或结局或两者原因的每个协变量;从该集合中排除任何已知为工具变量的变量;并将作为暴露和结果的共同原因的未测量变量的任何代理作为协变量。混杂因素选择的各种原则，然后进一步相关的统计协变量选择方法。
Selecting an appropriate set of confounders for which to control is critical for reliable causal inference. Recent theoretical and methodological developments have helped clarify a number of principles of confounder selection. When complete knowledge of a causal diagram relating all covariates to each other is available, graphical rules can be used to make decisions about covariate control. Unfortunately, such complete knowledge is often unavailable. This paper puts forward a practical approach to confounder selection decisions when thesomewhat less stringent assumption is made that knowledge is available for each covariate whether it is a cause of the exposure, and whether it is a cause of the outcome. Based on recent theoretically justified developments in the causal inference literature, the following proposal is made for covariate control decisions: control for each covariate that is a cause of the exposure, or of the outcome, or of both; exclude from this set any variable known to be an instrumental variable; and include as a covariate any proxy for an unmeasured variable that is a common cause of both the exposure and the outcome. Various principles of confounder selection are then further related to statistical covariate selection methods.