State-space Causal Models
State-Space Causal Models is a longitudinal causal inference framework that integrates state-space modeling with causal structure to distinguish latent temporal dynamics from causal interventions in time-series observational data.
11
Publications
1.4K
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27
Authors
21
Institutions
Publications per year
2017–2026
4
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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
Granger causality for state-space models
Lionel Barnett, Anil K. Seth · Physical Review E · 2015 · 229 citations · Full text
Causal learner: A toolbox for causal structure and Markov blanket learning
Zhaolong Ling, Kui Yu, Yiwen Zhang et al. · Pattern Recognition Letters · 2022 · 36 citations
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University College London
London, United Kingdom
1 h-index
3 publications
63 citations
Wellcome Centre for Human Neuroimaging
London, United Kingdom
1 h-index
3 publications
63 citations
University of Sussex
Brighton, United Kingdom
1 h-index
2 publications
458 citations
University of California, Davis
Davis, United States
1 h-index
1 publications
29 citations
Google (United States)
Mountain View, United States
1 h-index
4 publications
3.8K citations
The Institute of Statistical Mathematics
Tokyo, Japan
1 h-index
2 publications
22 citations

Vanderbilt University
Nashville, United States
1 h-index
1 publications
29 citations

Hefei University of Technology
Hefei, China
1 h-index
1 publications
36 citations
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PRE · 2 publications · 275 citations
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1 publications · 16 citations
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AOAS · 1 publications · 962 citations
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1 publications · 36 citations
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1 publications · 11 citations
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