title: Robust causal inference using directed acyclic graphs: the R package 'dagitty'.
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/].
authors.affiliation: Department of Tumour Immunology, Radboud University Medical Center, P.O. Box 9101, 6500 HB Nijmegen, The Netherlands.
authors.forename: Johannes
authors.initials: J
authors.lastname: Textor
authors.affiliation: Institute for Theoretical Computer Science, University of Luebeck, Luebeck, Germany.
authors.forename: Benito
authors.initials: B
authors.lastname: van der Zander
authors.affiliation: Leeds Institute of Cardiovascular and Metabolic Medicine.|Leeds Institute for Data Analytics, University of Leeds, Leeds, UK.
authors.forename: Mark S
authors.initials: MS
authors.lastname: Gilthorpe
authors.affiliation: Institute for Theoretical Computer Science, University of Luebeck, Luebeck, Germany.
authors.forename: Maciej
authors.initials: M
authors.lastname: Liskiewicz
authors.affiliation: Leeds Institute of Cardiovascular and Metabolic Medicine.|Leeds Institute for Data Analytics, University of Leeds, Leeds, UK.
authors.forename: George Th
authors.initials: GT
authors.lastname: Ellison
mesh_terms: D015982:Bias; D015984:Causality; D015986:Confounding Factors, Epidemiologic; D003627:Data Interpretation, Statistical; D004812:Epidemiologic Methods; D006801:Humans; D012984:Software