Results 1 to 10 of about 62,209 (271)
Independencies Induced from a Graphical Markov Model After Marginalization and Conditioning: The R Package ggm [PDF]
Journal of Statistical Software, 2006 We describe some functions in the R package ggm to derive from a given Markov model, represented by a directed acyclic graph, different types of graphs induced after marginalizing over and conditioning on some of the variables.
Giovanni M. Marchetti
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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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Covering Pairs in Directed Acyclic Graphs [PDF]
The Computer Journal, 2013 The Minimum Path Cover problem on directed acyclic graphs (DAGs) is a classical problem that provides a clear and simple mathematical formulation for several applications in different areas and that has an efficient algorithmic solution.
B.Y. Wu +7 more
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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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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.
europepmc +5 more sources
Reducing bias through directed acyclic graphs [PDF]
BMC Medical Research Methodology, 2008 Background The objective of most biomedical research is to determine an unbiased estimate of effect for an exposure on an outcome, i.e. to make causal inferences about the exposure.
Platt Robert W, Shrier Ian
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Vertex-pursuit in random directed acyclic graphs [PDF]
SIAM Journal on Discrete Mathematics, 2013 We examine a dynamic model for the disruption of information flow in hierarchical social networks by considering the vertex-pursuit game Seepage played in directed acyclic graphs (DAGs). In Seepage, agents attempt to block the movement of an intruder who
Bonato, Anthony +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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Causal Directed Acyclic Graphs to Mitigate Confounding Bias in Exposure‐Response Analyses [PDF]
CPT: Pharmacometrics & Systems PharmacologyThe use of exposure‐response (E‐R) analysis to support drug development and treatment individualization requires estimating the causal effect of drug exposure on response. This may be challenging when the E‐R relationship is confounded.
Sebastiaan C. Goulooze +4 more
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