Robust causal inference using directed acyclic graphs: the R package 'dagitty'
Robust causal inference using directed acyclic graphs: the R package 'dagitty'
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DOI：
10.1093/ije/dyw341
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发表时间：
2016-12-01
影响因子：
7.7
通讯作者：
Ellison, George T. H.
中科院分区：
文献类型：
--
作者：
Textor, Johannes;van der Zander, Benito;Ellison, George T. H.
有向无环图（DAG）提供了因果关系的系统表示，已成为流行病学中因果推理分析的既定框架，通常用于确定协变量调整集以最大限度地减少混杂偏差。 DAGitty 是一款流行的用于绘制和分析 DAG 的 Web 应用程序。在这里，我们介绍 R 包“dagitty”，它提供了对 R 平台内 DAGitty Web 应用程序的所有功能的访问，以进行统计计算，并且还提供了一些新功能。我们描述了 R 包“dagitty”如何用于： 评估 DAG 是否与其要表示的数据集一致；列举“统计上等效”但因果上不同的 DAG；并确定对于因果不同但统计上等效的 DAG 有效的暴露结果调整集。此功能使流行病学家能够检测 DAG 中的因果错误指定，并做出对一系列不同 DAG 仍然有效的可靠推论。
Directed acyclic graphs (DAGs), which offer systematic representations of causal relationships, have become an established framework for the analysis of causal inference in epidemiology, often being used to determine covariate adjustment sets for minimizing confounding bias. DAGitty is a popular web application for drawing and analysing DAGs. Here we introduce the R package 'dagitty', which provides access to all of the capabilities of the DAGitty web application within the R platform for statistical computing, and also offers several new functions. We describe how the R package 'dagitty' can be used to: evaluate whether a DAG is consistent with the dataset it is intended to represent; enumerate 'statistically equivalent' but causally different DAGs; and identify exposure-outcome adjustment sets that are valid for causally different but statistically equivalent DAGs. This functionality enables epidemiologists to detect causal misspecifications in DAGs and make robust inferences that remain valid for a range of different DAGs.