d-Separation is a formal criterion in causal graphical models that determines whether a set of variables is conditionally independent of another set given a third, by assessing the absence of active paths between them under a defined set of graphical blocking rules.
61
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68
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2017–2026
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Publications
61
Identifying independence in bayesian networks
Dan Geiger, Thomas Verma, Judea Pearl · Networks · 1990 · 526 citations
1–5 of 61
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106
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2
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1–5 of 106
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68
Tongji University
Shanghai, China
4 h-index
2 publications
48 citations
University of California, Los Angeles
Los Angeles, United States
3 h-index
6 publications
1.8K citations
Carnegie Mellon University
Pittsburgh, United States
3 h-index
3 publications
241 citations
Fudan University
Shanghai, China
3 h-index
3 publications
66 citations
University of Calgary
Calgary, Canada
2 h-index
1 publications
22 citations
Université de Sherbrooke
Sherbrooke, Canada
2 h-index
2 publications
633 citations
University of Science and Technology of China
Hefei, China
2 h-index
2 publications
25 citations
University of Toronto
Toronto, Canada
2 h-index
2 publications
50 citations
1–8 of 68
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- arXiv (Cornell University)
Repository · 15 publications · 651 citations
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- Proceedings of the AAAI Conference on Artificial Intelligence
Conference · 3 publications · 54 citations
4
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- New Journal of Physics
NJP · 3 publications · 238 citations
3
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- Structural Equation Modeling A Multidisciplinary Journal
SEM · 3 publications · 159 citations
3
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- Research Showcase @ Carnegie Mellon University (Carnegie Mellon University)
Repository · 2 publications · 35 citations
2
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1–5