Causality Algorithms

Causality Algorithms is a computational method designed to infer or evaluate causal relationships among variables by systematically applying logical, statistical, or machine learning principles to distinguish genuine cause-effect interactions from mere associations.

193

Publications

12.7K

Citations

882

Authors

370

Institutions

Publications per year

2017–2026

102

Authors

882

  1. KY
    Kui Yu

    Hefei University of Technology, China

    6 publications · 269 citations

    6

    h-index

  2. CG
    Clark Glymour

    Carnegie Mellon University, United States

    6 publications · 254 citations

    6

    h-index

  3. DJ
    Dominik Janzing

    Max Planck Institute for Intelligent Systems, Germany

    5 publications · 252 citations

    5

    h-index

  4. BS
    Bernhard Schölkopf

    Max Planck Institute for Intelligent Systems, Germany

    5 publications · 231 citations

    5

    h-index

  5. LL
    Lin Liu

    University of South Australia, Australia

    4 publications · 218 citations

    4

    h-index

1–5 of 882

Venues

  1. arXiv (Cornell University)

    Repository · 23 publications · 1.3K citations

    16

    h-index

  2. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 6 publications · 186 citations

    6

    h-index

  3. PLoS ONE

    4 publications · 196 citations

    4

    h-index

  4. PubMed

    Repository · 4 publications · 166 citations

    4

    h-index

  5. Neurocomputing

    3 publications · 244 citations

    3

    h-index

1–5