Robust causal inference using directed acyclic graphs: the R package 'dagitty'.
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...
| Publicado en: | International Journal of Epidemiology Vol. 45; no. 6; pp. 1887 - 1895 |
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| Autores principales: | , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Oxford University Press / USA
Dec2016
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=122195267&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 122195267 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 03005771 DIH jtl: International Journal of Epidemiology issn: 03005771 maglogo: N pubinfo: dt: Dec2016 vid: 45 iid: 6 pid: 622 pub: Oxford University Press / USA artinfo: ui: 122195267 122195267 NLM28089956 10.1093/ije/dyw341 NLM28089956 122195267 ppf: 1887 ppct: 8 formats: tig: atl: Robust causal inference using directed acyclic graphs: the R package 'dagitty'. aug: au: Textor, Johannes van der Zander, Benito Gilthorpe, Mark S. Lićkiewicz, Maciej Ellison, George T. H. Liskiewicz, Maciej Ellison, George Th affil: Department of Tumour Immunology, Radboud University Medical Center, P.O. Box 9101, 6500 HB Nijmegen, The Netherlands sug: ab: 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/]. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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