Active Causal Discovery
Active Causal Discovery is a methodological framework within causal discovery that actively selects and intervenes on variables in the system to iteratively acquire data that maximally inform the identification of causal structures, distinguishing itself from passive observational approaches by enabling targeted experimentation to reduce uncertainty in causal model estimation.
2
Publications
45
Citations
15
Authors
2
Institutions
Publications per year
2017–2026
2
Parents
2
Learning Neural Causal Models with Active Interventions
Nino Scherrer, Olexa Bilaniuk, Yashas Annadani et al. · arXiv (Cornell University) · 2021 · 16 citations · Full text
1–4 of 2
15
1 publications · 16 citations
1
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1
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1
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Max Planck Institute for Intelligent Systems, Germany
1 publications · 16 citations
1
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1
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1–5 of 15
2
State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation
Chengdu, China
1 h-index
4 publications
116 citations
Southwest Petroleum University
Chengdu, China
1 h-index
2 publications
58 citations

Central South University
Changsha, China
1 publications
18 citations
Hunan Communications Research Institute
Changsha, China
1 publications
18 citations
New York University
New York, United States
1 publications
12 citations
University Medical Center Utrecht
Utrecht, The Netherlands
1 publications
12 citations
Minnesota Department of Health
Saint Paul, United States
1 publications
12 citations

University of Minnesota
Minneapolis, United States
1 publications
12 citations
1–8 of 2
1 publications · 29 citations
1
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Repository · 1 publications · 16 citations
1
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1 publications · 18 citations
1 publications · 12 citations
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