Comparison of open-source software for producing directed acyclic graphs

Pitts Amy J. () and Fowler Charlotte R.
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Pitts Amy J.: Department of Biostatistics, Columbia University, New York, NY 10027, United States
Fowler Charlotte R.: Department of Biostatistics, Columbia University, New York, NY 10027, United States

Journal of Causal Inference, 2024, vol. 12, issue 1, 10

Abstract: Many software packages have been developed to assist researchers in drawing directed acyclic graphs (DAGs), each with unique functionality and usability. We examine five of the most common software to generate DAGs: TikZ, DAGitty, ggdag, dagR, and igraph. For each package, we provide a general description of its background, analysis and visualization capabilities, and user-friendliness. In addition, in order to compare packages, we produce two DAGs in each software, the first featuring a simple confounding structure and the second with a more complex structure with three confounders and a mediator. We provide recommendations for when to use each software depending on the user’s needs.

Keywords: directed acyclic graphs; TikZ; DAGitty; ggdag; dagR; igraph (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:bpj:causin:v:12:y:2024:i:1:p:10:n:1

DOI: 10.1515/jci-2023-0031

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