Department of Epidemiology, Harvard T.H. Chan School of Public Health, 677 Huntington Avenue, Boston, MA, 02115, USA.
Eur J Epidemiol. 2019 Mar;34(3):211-219. doi: 10.1007/s10654-019-00494-6. Epub 2019 Mar 6.
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 the somewhat 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.
选择适当的混杂因素集进行控制对于可靠的因果推断至关重要。最近的理论和方法学发展帮助澄清了混杂因素选择的一些原则。当有关所有协变量之间关系的因果图的完整知识可用时，可以使用图形规则来做出关于协变量控制的决策。不幸的是，这种完整的知识通常是不可用的。本文提出了一种实用的混杂因素选择决策方法，当假设存在一些不太严格的条件时，即对于每个协变量，无论是暴露的原因，还是结果的原因，都有知识可用。基于因果推断文献中最近理论上合理的发展，本文提出了协变量控制决策的建议：控制每个协变量是暴露的原因，或结果的原因，或两者的原因；从这个集合中排除任何已知的工具变量；并将任何未测量变量的代理变量作为暴露和结果的共同原因包含在协变量中。然后，将混杂因素选择的各种原则进一步与统计协变量选择方法相关联。
