Probabilistic Causal Inference
Probabilistic Causal Inference is a computational framework that belongs to the class of causal inference methods and distinguishes itself by formally quantifying uncertainty in causal relationships through probabilistic models to identify likely causal variants in genetic fine-mapping studies.
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15K
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550
Authors
312
Institutions
Publications per year
2017–2026
61
Parents
177
Donald B. Rubin · Statistical Science · 1990 · 665 citations · Full text
Identifying Causal Variants at Loci with Multiple Signals of Association
Farhad Hormozdiari, Emrah Kostem, Eun Yong Kang et al. · Genetics · 2014 · 562 citations · Full text
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University of California, Los Angeles
Los Angeles, United States
9 h-index
18 publications
4.7K citations
University of California, Berkeley
Berkeley, United States
6 h-index
10 publications
568 citations
Max Planck Society
Munich, Germany
5 h-index
11 publications
864 citations
University College London
London, United Kingdom
5 h-index
10 publications
1K citations
ETH Zurich
Zurich, Switzerland
4 h-index
7 publications
1.4K citations
New York University
New York, United States
4 h-index
5 publications
200 citations
University of Washington
Seattle, United States
4 h-index
9 publications
793 citations
Harvard University
Cambridge, United States
4 h-index
15 publications
442 citations
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Repository · 5 publications · 185 citations
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5 publications · 195 citations
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3 publications · 627 citations
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3 publications · 3.4K citations
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3 publications · 232 citations
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