Graphical Causal Models
Graphical Causal Models is a formal framework that represents causal relationships among variables using directed acyclic graphs, where nodes denote variables and edges encode direct causal influences, enabling the identification and analysis of causal effects from observational and experimental data.
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Publications
61.6K
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1.6K
Authors
697
Institutions
Publications per year
2017–2026
269
Parents
733
Causal Diagrams for Epidemiologic Research
Sander Greenland, Judea Pearl, James M. Robins · Epidemiology · 1999 · 3.7K citations
Causal diagrams for empirical research
Judea Pearl · Biometrika · 1995 · 2.3K citations
Reducing bias through directed acyclic graphs
Ian Shrier, Robert W. Platt · BMC Medical Research Methodology · 2008 · 1.4K citations · Full text
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University of California, Los Angeles
Los Angeles, United States
27 h-index
55 publications
7.8K citations
Harvard University
Cambridge, United States
23 h-index
52 publications
19.4K citations
Carnegie Mellon University
Pittsburgh, United States
23 h-index
71 publications
6.9K citations
ETH Zurich
Zurich, Switzerland
16 h-index
39 publications
7.5K citations
University of Washington
Seattle, United States
14 h-index
22 publications
2.8K citations
University College London
London, United Kingdom
14 h-index
30 publications
6.6K citations
Massachusetts Institute of Technology
Cambridge, United States
12 h-index
18 publications
811 citations

Radboud University Nijmegen
Nijmegen, The Netherlands
12 h-index
23 publications
3.5K citations
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Repository · 85 publications · 3K citations
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Repository · 11 publications · 883 citations
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