Robust causal inference using directed acyclic graphs: the R package ‘dagitty’
Open accessAbstract
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/].
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A directed acyclic graph was constructed to define a priori assumptions about covariate relationships with exposure and outcome.
“We then constructed a directed acyclic graph (DAG) to clearly define our a priori assumptions regarding covariate relationships with the exposure and outcome variables (Figure 1).29,30”
From Receipt of Glucagon-Like Peptide-1 Receptor Agonists Among Rural Patients with Obstructive Sleep Apnea and Excess Weight (2026) · cites this paper as [30]
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- Authors
- Textor, J., van der Zander, B., Gilthorpe, M. S., Liśkiewicz, M., & Ellison, G. T. H.
- Affiliations
- Radboud University Medical Center (NL)
- University of Leeds (GB)
- University of Lübeck (DE)
- Corresponding author
- Textor, Johannes
- Venue
- International Journal of Epidemiology
- Publisher
- Oxford University Press (OUP)
- Published
- 2017-01-15
- Volume
- 45
- Issue
- 6
- Pages
- dyw341
- ISSN
- 0300-5771 (ISSN-L)
- 1464-3685
- OpenAlex
- W2576536258
- Cited by
- 2,295
- References
- 19
- Citations by year
- 2026: 156 · 2025: 409 · 2024: 451 · 2023: 392 · 2022: 323 · 2021: 256 · 2020: 201 · 2019: 74 · 2018: 22 · 2017: 6 · 2016: 2 · 2012: 1
- FWCI
- 35.46
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- International Journal of Epidemiology (publishedVersion)
- PubMed (publishedVersion)
- White Rose Research Online (University of Leeds, The University of Sheffield, University of York) (acceptedVersion)
- Radboud Repository (Radboud University) (submittedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Keywords
- directed acyclic graphs, causal inference, R package, adjustment sets, confounding
- MeSH terms
- Data Interpretation, Statistical, Epidemiologic Methods, Humans, Software, Bias, Causality, Confounding Factors, Epidemiologic
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