Directed acyclic graphs for clinical research: a tutorial [PDF]
Journal of Minimally Invasive Surgery, 2023 Directed acyclic graphs (DAGs) are useful tools for visualizing the hypothesized causal structures in an intuitive way and selecting relevant confounders in causal inference.
Sangmin Byeon, Woojoo Lee
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Tutorial on directed acyclic graphs. [PDF]
J Clin Epidemiol, 2022 Directed acyclic graphs (DAGs) are an intuitive yet rigorous tool to communicate about causal questions in clinical and epidemiologic research and inform study design and statistical analysis. DAGs are constructed to depict prior knowledge about biological and behavioral systems related to specific causal research questions.
Digitale JC, Martin JN, Glymour MM.
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Reducing bias in experimental ecology through directed acyclic graphs [PDF]
Ecology and Evolution, 2023 Ecologists often rely on randomized control trials (RCTs) to quantify causal relationships in nature. Many of our foundational insights of ecological phenomena can be traced back to well‐designed experiments, and RCTs continue to provide valuable ...
Suchinta Arif, Melanie Duc Bo Massey
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Cycle analysis of Directed Acyclic Graphs
Physica A: Statistical Mechanics and Its Applications, 2022 In this paper, we employ the decomposition of a directed network as an undirected graph plus its associated node metadata to characterise the cyclic structure found in directed networks by finding a Minimal Cycle Basis of the undirected graph and augment its components with direction information. We show that only four classes of directed cycles exist,
Paul Expert +2 more
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Comparison of open-source software for producing directed acyclic graphs [PDF]
Journal of Causal InferenceMany 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.
Pitts Amy J., Fowler Charlotte R.
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SEMdag: Fast learning of Directed Acyclic Graphs via node or layer ordering. [PDF]
PLoS ONEA Directed Acyclic Graph (DAG) offers an easy approach to define causal structures among gathered nodes: causal linkages are represented by arrows between the variables, leading from cause to effect.
Mario Grassi, Barbara Tarantino
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Inferring Regulatory Networks From Mixed Observational Data Using Directed Acyclic Graphs
Frontiers in Genetics, 2020 Construction of regulatory networks using cross-sectional expression profiling of genes is desired, but challenging. The Directed Acyclic Graph (DAG) provides a general framework to infer causal effects from observational data. However, most existing DAG
Karen Mohlke, Di Wu, Taylor Poston
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In reference to ‘Directed acyclic graphs for clinical research: a tutorial’ [PDF]
Journal of Minimally Invasive Surgery, 2023 Anjali Rajkumar, Vishak MS
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Constructing causal pathways for premature cardiovascular disease mortality using directed acyclic graphs with integrating evidence synthesis and expert knowledge [PDF]
Scientific ReportsCardiovascular disease (CVD) is a major global cause of premature mortality. While multiple studies propose CVD mortality prediction models based on regression frameworks, incorporating causal understanding through causal inference approaches can enhance
Wan Shakira Rodzlan Hasani +3 more
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Contextual Directed Acyclic Graphs
CoRR, 2023 To appear in the Proceedings of the 27th International Conference on Artificial Intelligence and ...
Ryan Thompson +2 more
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