Probabilistic Causal Inference

Probabilistic Causal Inference is a computational framework that belongs to the class of causal inference methods and distinguishes itself by formally quantifying uncertainty in causal relationships through probabilistic models to identify likely causal variants in genetic fine-mapping studies.

177

Publications

15K

Citations

550

Authors

312

Institutions

Publications per year

2017–2026

61

Authors

550

  1. LS
    Ladan Shams

    University of California, Los Angeles, United States

    5 publications · 738 citations

    5

    h-index

  2. PW
    Patricia W. Cheng

    University of California, Los Angeles, United States

    4 publications · 536 citations

    4

    h-index

  3. DJ
    Dominik Janzing

    Max Planck Institute for Intelligent Systems, Germany

    3 publications · 145 citations

    3

    h-index

  4. MJ
    Marek J. Drużdżel

    University of Pittsburgh, United States

    3 publications · 111 citations

    3

    h-index

  5. SK
    Stig Kjær Andersen

    Aalborg University, Denmark

    3 publications · 231 citations

    3

    h-index

1–5 of 550

Venues

  1. arXiv (Cornell University)

    Repository · 5 publications · 185 citations

    5

    h-index

  2. Scientific Reports

    5 publications · 195 citations

    5

    h-index

  3. Genetics

    3 publications · 627 citations

    3

    h-index

  4. Journal of the American Statistical Association

    3 publications · 3.4K citations

    3

    h-index

  5. Synthese

    3 publications · 232 citations

    3

    h-index

1–5