Graphical Causal Models

Graphical Causal Models is a formal framework that represents causal relationships among variables using directed acyclic graphs, where nodes denote variables and edges encode direct causal influences, enabling the identification and analysis of causal effects from observational and experimental data.

733

Publications

61.6K

Citations

1.6K

Authors

697

Institutions

Publications per year

2017–2026

269

Authors

1.6K

  1. JP
    Judea Pearl

    University of California, Los Angeles, United States

    36 publications · 9.8K citations

    28

    h-index

  2. IS
    Ilya Shpitser

    Johns Hopkins University, United States

    19 publications · 1.4K citations

    15

    h-index

  3. PB
    Peter Bühlmann

    ETH Zurich, Switzerland

    13 publications · 2K citations

    13

    h-index

  4. PS
    Peter Spirtes

    Carnegie Mellon University, United States

    16 publications · 1.7K citations

    13

    h-index

  5. JM
    James M. Robins

    Harvard University, United States

    17 publications · 5.2K citations

    13

    h-index

1–5 of 1.6K

Venues

  1. arXiv (Cornell University)

    Repository · 85 publications · 3K citations

    31

    h-index

  2. International Journal of Approximate Reasoning

    12 publications · 384 citations

    11

    h-index

  3. Biometrika

    10 publications · 2.9K citations

    10

    h-index

  4. eScholarship (California Digital Library)

    Repository · 11 publications · 883 citations

    10

    h-index

  5. Epidemiology

    9 publications · 5.5K citations

    9

    h-index

1–5