Directed Acyclic Graph Learning
Directed Acyclic Graph Learning is a computational method in causal discovery that identifies the structure of a directed acyclic graph from observational data by inferring causal relationships among variables while enforcing acyclicity constraints.
296
Publications
18.3K
Citations
791
Authors
339
Institutions
Publications per year
2017–2026
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Parents
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Carnegie Mellon University
Pittsburgh, United States
16 h-index
32 publications
4.3K citations
Massachusetts Institute of Technology
Cambridge, United States
8 h-index
20 publications
809 citations
National University of Singapore
Singapore, Singapore
8 h-index
15 publications
669 citations

Peking University
Beijing, China
6 h-index
14 publications
684 citations
University of Siena
Siena, Italy
6 h-index
17 publications
882 citations

Beijing University of Posts and Telecommunications
Beijing, China
6 h-index
16 publications
2.8K citations
ETH Zurich
Zurich, Switzerland
6 h-index
11 publications
1.3K citations
Huawei Technologies (China)
Shenzhen, China
5 h-index
15 publications
499 citations
1–8 of 339
Repository · 52 publications · 1.8K citations
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JMLR · 6 publications · 715 citations
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Conference · 5 publications · 215 citations
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5 publications · 1.8K citations
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Conference · 5 publications · 353 citations
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