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Publication Details
AFRICAN RESEARCH NEXUS
SHINING A SPOTLIGHT ON AFRICAN RESEARCH
medicine
Robust causal inference using directed acyclic graphs: The R package 'dagitty'
International Journal of Epidemiology, Volume 45, No. 6, Article dyw341, Year 2016
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Description
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 exposureoutcome 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. The R package 'dagitty' is available through the comprehensive R archive network (CRAN) at [https://cran.r-project.org/web/packages/dagitty/]. The source code is available on github at [https://github.com/jtextor/dagitty]. The web application 'DAGitty' is free software, licensed under the GNU general public licence (GPL) version 2 and is available at [http:// dagitty.net/]. © The Author 2017.
Authors & Co-Authors
Gilthorpe, Mark S.
United Kingdom, Leeds
University of Leeds
Ellison, George T.H.
United Kingdom, Leeds
University of Leeds
Statistics
Citations: 1,056
Authors: 2
Affiliations: 3
Identifiers
Doi:
10.1093/ije/dyw341
ISSN:
03005771
Research Areas
Health System And Policy