Causal Representation
Causal Representation is a formal modeling framework that specifies the causal dependencies among variables in a system, distinguishing itself from associative or statistical representations by encoding directed influence relationships grounded in interventionist or counterfactual semantics.
361
Publications
21.7K
Citations
967
Authors
418
Institutions
Publications per year
2017–2026
146
Parents
361
Toward Causal Representation Learning
Bernhard Schölkopf, Francesco Locatello, Stefan Bauer et al. · Proceedings of the IEEE · 2021 · 1K citations · Full text
Phillip Wolff, Aron K. Barbey · Frontiers in Human Neuroscience · 2015 · 794 citations · Full text
Virtual Worlds as Fuzzy Cognitive Maps
Julie Dickerson, Bart Kosko · PRESENCE Virtual and Augmented Reality · 1994 · 427 citations
1–5 of 361
967
Max Planck Institute for Intelligent Systems, Germany
6 publications · 1.2K citations
6
h-index
Massachusetts Institute of Technology, United States
5 publications · 182 citations
5
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5
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5
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5
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1–5 of 967
418
Stanford University
Stanford, United States
11 h-index
20 publications
1.1K citations
Carnegie Mellon University
Pittsburgh, United States
11 h-index
11 publications
557 citations
University College London
London, United Kingdom
10 h-index
20 publications
771 citations
Massachusetts Institute of Technology
Cambridge, United States
10 h-index
20 publications
2.1K citations
Yale University
New Haven, United States
7 h-index
11 publications
522 citations
New York University
New York, United States
7 h-index
9 publications
524 citations
The University of Texas at Austin
Austin, United States
7 h-index
12 publications
727 citations
University of Oxford
Oxford, United Kingdom
6 h-index
26 publications
3K citations
1–8 of 418
Repository · 32 publications · 920 citations
18
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15 publications · 1K citations
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Repository · 8 publications · 120 citations
7
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6 publications · 994 citations
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Conference · 5 publications · 280 citations
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