Graph Identifiability
Graph Identifiability is a structural property of a directed acyclic graph that determines whether the causal structure can be uniquely inferred from the observed joint probability distribution under a given family of structural equation models.
35
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1.9K
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63
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48
Institutions
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2017–2026
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Causal discovery with continuous additive noise models
Jonas Peters, Joris M. Mooij, Dominik Janzing et al. · Journal of Machine Learning Research · 2014 · 284 citations
Causal Discovery with Continuous Additive Noise Models
Jonas Peters, Joris M. Mooij, Dominik Janzing et al. · UvA-DARE (University of Amsterdam) · 2013 · 156 citations · Full text
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University of California, Los Angeles
Los Angeles, United States
4 h-index
9 publications
1.3K citations
ETH Zurich
Zurich, Switzerland
3 h-index
3 publications
543 citations

Radboud University Nijmegen
Nijmegen, The Netherlands
3 h-index
3 publications
504 citations

University of Amsterdam
Amsterdam, The Netherlands
2 h-index
2 publications
440 citations

Sapienza University of Rome
Rome, Italy
2 h-index
2 publications
32 citations
North Carolina State University
Raleigh, United States
2 h-index
4 publications
226 citations
Max Planck Society
Munich, Germany
2 h-index
5 publications
760 citations
Universität Greifswald
Greifswald, Germany
1 h-index
1 publications
34 citations
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Repository · 5 publications · 262 citations
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Repository · 3 publications · 540 citations
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Repository · 2 publications · 63 citations
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2 publications · 30 citations
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2 publications · 97 citations
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