Testing graphical causal models using the R package “dagitty”
Open accessAbstract
Causal diagrams such as directed acyclic graphs (DAGs) are used in several scientific fields to help design and analyze studies that aim to infer causal effects from observational data; for example, DAGs can help identify suitable strategies to reduce confounding bias. However, DAGs can be difficult to design, and the validity of any DAG-derived strategy hinges on the validity of the postulated DAG itself. Researchers should therefore check whether the assumptions encoded in the DAG are consistent with the data before proceeding with the analysis. Here, we explain how the R package 'dagitty', based on the web tool dagitty.net, can be used to test the statistical implications of the assumptions encoded in a given DAG. We hope that this will help researchers discover model specification errors, avoid erroneous conclusions, and build better models. © 2021 The Authors. Basic Protocol 1: Constructing and importing DAG models from the dagitty web interface Support Protocol 1: Installing R, RStudio, and the dagitty package Basic Protocol 2: Testing DAGs against categorical data Basic Protocol 3: Testing DAGs against continuous data Support Protocol 2: Testing DAGs against continuous data with non-linearities Basic Protocol 4: Testing DAGs against a combination of categorical and continuous data.
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To facilitate causal inference, a directed acyclic graph (DAG), constructed using dagitty, was used for model selection.
“To facilitate causal inference, we used a directed acyclic graph (DAG), constructed using dagitty (Ankan et al., 2021), for model selection (Fig. S1).”
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- Authors
- Ankan, A., Wortel, I. M. N., & Textor, J.
- Affiliations
- Radboud University Nijmegen (NL)
- Corresponding author
- Textor, Johannes
- Venue
- Current Protocols
- Publisher
- Wiley
- Published
- 2021-02
- Volume
- 1
- Issue
- 2
- Pages
- e45-e45
- ISSN
- 2691-1299 (ISSN-L)
- OpenAlex
- W3131826007
- Cited by
- 86
- References
- 11
- Citations by year
- 2026: 13 · 2025: 15 · 2024: 20 · 2023: 21 · 2022: 16 · 2021: 1
- FWCI
- 11.00
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Current Protocols (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Radboud Repository (Radboud University) (submittedVersion)
- Radboud Repository (Radboud University) (submittedVersion, other-oa)
- Funders
- Radboud Universitair Medisch Centrum
- Kurt Weill Foundation for Music
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
- Social Sciences › Arts and Humanities › History and Philosophy of Science › Philosophy and History of Science
- Keywords
- causal graphical models, directed acyclic graphs, causal inference, specification testing, confounding, observational data, nonlinear relationship
- MeSH terms
- Data Interpretation, Statistical, Models, Theoretical, Bias, Causality, Confounding Factors, Epidemiologic
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