Score-based Causal Discovery
Score-Based Causal Discovery is a method for estimating causal graphical models by assigning a score to each candidate graph based on how well it fits the observational data while penalizing complexity, and then selecting the graph with the optimal score.
24
Publications
935
Citations
74
Authors
36
Institutions
Publications per year
2017–2026
15
Variants
Score-based Structure Learning
24
Generalized Score Functions for Causal Discovery
Biwei Huang, Kun Zhang, Yizhu Lin et al. · 2018 · 107 citations · Full text
Using causal discovery for feature selection in multivariate numerical time series
Youqiang Sun, Jiuyong Li, Jixue Liu et al. · Machine Learning · 2014 · 92 citations
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36
Carnegie Mellon University
Pittsburgh, United States
4 h-index
8 publications
519 citations
University of Pittsburgh
Pittsburgh, United States
4 h-index
9 publications
141 citations

University of Wisconsin–Milwaukee
Milwaukee, United States
2 h-index
2 publications
49 citations
Huawei Technologies (China)
Shenzhen, China
2 h-index
5 publications
265 citations

University of Warwick
Coventry, United Kingdom
1 h-index
2 publications
68 citations

City, University of London
London, United Kingdom
1 h-index
2 publications
68 citations
Institut polytechnique de Grenoble
Grenoble, France
1 h-index
3 publications
363 citations
Southwest University
Chongqing, China
1 h-index
2 publications
82 citations
1–8 of 36
Repository · 8 publications · 237 citations
8
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1 publications · 14 citations
1
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1 publications · 17 citations
1
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1 publications · 41 citations
1
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JAIR · 1 publications · 121 citations
1
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