Bayesian Causal Effect Estimation
Bayesian Causal Effect Estimation is a statistical framework that uses Bayesian inference to estimate causal effects by incorporating prior knowledge and modeling uncertainty in both observed data and unobserved confounding structures.
217
Publications
14.6K
Citations
638
Authors
338
Institutions
Publications per year
2017–2026
97
Parents
217
Inferring causal impact using Bayesian structural time-series models
Kay H. Brodersen, Fabian Gallusser, Jim Koehler et al. · The Annals of Applied Statistics · 2015 · 962 citations · Full text
Ying Yuan, David P. MacKinnon · Psychological Methods · 2009 · 625 citations · Full text
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338
Harvard University
Cambridge, United States
11 h-index
22 publications
1.3K citations

University of British Columbia
Vancouver, Canada
10 h-index
15 publications
1.8K citations
University of Michigan
Ann Arbor, United States
9 h-index
15 publications
780 citations
The University of Texas at Austin
Austin, United States
9 h-index
11 publications
1.1K citations

Imperial College London
London, United Kingdom
8 h-index
14 publications
690 citations
University of Pennsylvania
Philadelphia, United States
7 h-index
12 publications
901 citations
Arizona State University
Tempe, United States
6 h-index
10 publications
1.4K citations

University of Minnesota
Minneapolis, United States
6 h-index
11 publications
175 citations
1–8 of 338
13 publications · 635 citations
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14 publications · 517 citations
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AOAS · 9 publications · 1.2K citations
9
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7 publications · 244 citations
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Repository · 7 publications · 109 citations
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