Causal Diagrams: Pitfalls and Tips
Causal Diagrams: Pitfalls and Tips
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DOI：
10.2188/jea.je20190192
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发表时间：
2020-04-01
影响因子：
4.7
通讯作者：
Yamamoto, Eiji
中科院分区：
文献类型：
--
作者：
Suzuki, Etsuji;Shinozaki, Tomohiro;Yamamoto, Eiji
图形模型是因果推理的有用工具，而因果有向无环图(DAG)被广泛用于确定足以控制混淆的变量以估计因果效应。我们讨论了在使用DAG时容易忽视的以下十个陷阱和提示：1)DAG上的每个节点对应一个随机变量，而不是其已实现的值；2)DAG中箭头的存在或不存在对应于总体中个体因果效应的存在或不存在；3)“不可操纵”变量及其箭头应谨慎绘制；4)最好是为总体绘制DAG，而不是为暴露或未暴露的群体绘制DAG；5)DAG主要用于在预期混淆的概念下检查分布中混淆的存在；6)虽然DAG提供了因果结构的定性差异，但它们不能描述如何针对混淆进行调整的细节；7)DAG可用于说明匹配的后果以及在队列和病例对照研究中对匹配变量的适当处理；8)当明确说明DAG中的时间顺序时，有必要为每个时刻使用单独的节点；9)在某些情况下，带符号边的DAG可用于得出关于偏差方向的结论；以及10)DAG可用于(且应当)不仅用于描述混淆偏差，而且还用于描述其他形式的偏差。我们还讨论了图形模型的最新发展和未来的发展方向。
Graphical models are useful tools in causal inference, and causal directed acyclic graphs (DAGs) are used extensively to determine the variables for which it is sufficient to control for confounding to estimate causal effects. We discuss the following ten pitfalls and tips that are easily overlooked when using DAGs: 1) Each node on DAGs corresponds to a random variable and not its realized values; 2) The presence or absence of arrows in DAGs corresponds to the presence or absence of individual causal effect in the population; 3) "Non-manipulable" variables and their arrows should be drawn with care; 4) It is preferable to draw DAGs for the total population, rather than for the exposed or unexposed groups; 5) DAGs are primarily useful to examine the presence of confounding in distribution in the notion of confounding in expectation; 6) Although DAGs provide qualitative differences of causal structures, they cannot describe details of how to adjust for confounding; 7) DAGs can be used to illustrate the consequences of matching and the appropriate handling of matched variables in cohort and case-control studies; 8) When explicitly accounting for temporal order in DAGs, it is necessary to use separate nodes for each timing; 9) In certain cases, DAGs with signed edges can be used in drawing conclusions about the direction of bias; and 10) DAGs can be (and should be) used to describe not only confounding bias but also other forms of bias. We also discuss recent developments of graphical models and their future directions.