Textor, J, van der Zander, B, Gilthorpe, MS orcid.org/0000-0001-8783-7695 et al. (2 more authors) (2017) Robust causal inference using directed acyclic graphs: the R package ‘dagitty’. International Journal of Epidemiology, 45 (6). pp. 1887-1894. ISSN 0300-5771
Abstract
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. 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/].
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © The Author 2017; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association. This is a pre-copyedited, author-produced version of an article accepted for publication in International Journal of Epidemiology following peer review. The version of record Johannes Textor, Benito van der Zander, Mark S. Gilthorpe, Maciej Liśkiewicz, George T.H. Ellison; Robust causal inference using directed acyclic graphs: the R package ‘dagitty’. Int J Epidemiol 2017 dyw341. doi: 10.1093/ije/dyw341 is available online at: https://doi.org/10.1093/ije/dyw341 |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Medicine and Health (Leeds) > School of Medicine (Leeds) > Leeds Institute of Cardiovascular and Metabolic Medicine (LICAMM) > Clinical & Population Science Dept (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 14 Dec 2016 16:19 |
Last Modified: | 01 Feb 2019 12:33 |
Published Version: | https://doi.org/10.0.4.69/ije/dyw341 |
Status: | Published |
Publisher: | Oxford University Press |
Identification Number: | 10.1093/ije/dyw341 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:109517 |