DAGitty: a graphical tool for analyzing causal diagrams.

By Johannes Textor, J. Hardt, S. Knüppel. Epidemiology, 2011

1,189 citations recorded by the source corpus.

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Use of directed acyclic graphs (DAGs) to identify confounders in applied health research: review and recommendations (2020, methodology): “Articles citing DAGitty online or the DAGitty R software packages were also identified.(15,21) Results were restricted to ‘original articles’ involving human participants (Medline and Embase) indexed within medicine, health, social science, psychology, dentistry, nursing and related fields.”
Interpretation and identification of causal mediation. (2014, neutral): “…a given context entails are advised to do so through the tool of d-separation (gently introduced in Appendix A) but this is not absolutely necessary, since d-separation and other graph-based techniques are mechanized on several available software programs (e.g., Kyono, 2010; Textor et al., 2011).”
Robust causal inference using directed acyclic graphs: the R package 'dagitty'. (2017, background): “For these analyses it would be more e ﬃ cient to have DAGitty’s functionality embedded within the statistical software used.”
Graphical Representation of Missing Data Problems (2015, methodology): “DAGitty does not support drawing of bidirected arrows, however they can be easily represented as unobserved variables that have direct effects on both variables that should be connected with a bidirected arrow.”