Robust causal inference using directed acyclic graphs: the R package 'dagitty'.

By Johannes Textor, Benito van der Zander, Mark S. Gilthorpe, M. Liskiewicz, G. Ellison. International Journal of Epidemiology, 2017

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D’ya Like DAGs? A Survey on Structure Learning and Causal Discovery (2021, background): “causaleffect [240] general causality, R daggity [237] general causality, R dosearch [239] causal effect identification, R Causal Discovery Toolbox [120] causal discovery, Python pcalg [122] causal discovery, R bnlearn [215] causal discovery, R rEDM [181] dynamic modeling and convergent cross mapping”
Reporting guidelines for Human Microbiome Research: the STORMS checklist (2021, background): “As part of this theoretical justification, authors should consider including a directed acyclic graph showing the hypothesized causal relationships of interest 35,36 .”
Use of directed acyclic graphs (DAGs) to identify confounders in applied health research: review and recommendations (2020, methodology): “Data were extracted on the reporting of: estimands, DAGs and adjustment sets, alongside the characteristics of each article’s largest DAG.”