Statistical Causality Assessment
Statistical Causality Assessment is a formal inferential framework within genetic fine-mapping that distinguishes causal variants from non-causal associations by integrating statistical evidence of association with biological priors and probabilistic models to quantify the likelihood of causality at a given genomic locus.
22
Publications
1.2K
Citations
48
Authors
37
Institutions
Publications per year
2017–2026
8
Parents
22
Stephen J. Majeski, David Jones · Journal of Conflict Resolution · 1981 · 101 citations
Glenn Withers, David Pope · Economic Record · 1985 · 96 citations
1–5 of 22
48
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1–5 of 48
37
University of Cambridge
Cambridge, United Kingdom
3 h-index
4 publications
144 citations
Harvard University
Cambridge, United States
1 h-index
1 publications
81 citations

La Trobe University
Melbourne, Australia
1 h-index
1 publications
41 citations
University of Illinois System
Urbana, United States
1 h-index
1 publications
101 citations

The University of Queensland
Brisbane, Australia
1 h-index
1 publications
13 citations
Guangxi Medical University
Nanning, China
1 h-index
10 publications
250 citations
Guangxi University
Nanning, China
1 h-index
10 publications
250 citations
European Central Bank
Frankfurt am Main, Germany
1 h-index
2 publications
24 citations
1–8 of 37
2 publications · 139 citations
2
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2 publications · 82 citations
2
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2 publications · 137 citations
2
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SMJ · 1 publications · 82 citations
1
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JTSCM · 1 publications · 20 citations
1
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1–5