Graph Identifiability

Graph Identifiability is a structural property of a directed acyclic graph that determines whether the causal structure can be uniquely inferred from the observed joint probability distribution under a given family of structural equation models.

35

Publications

1.9K

Citations

63

Authors

48

Institutions

Publications per year

2017–2026

7

Authors

63

  1. JP
    Judea Pearl

    University of California, Los Angeles, United States

    5 publications · 623 citations

    5

    h-index

  2. JP
    Jonas Peters

    ETH Zurich, Switzerland

    3 publications · 504 citations

    3

    h-index

  3. JM
    Joris M. Mooij

    Radboud University Nijmegen, Netherlands

    3 publications · 504 citations

    3

    h-index

  4. DJ
    Dominik Janzing

    Max Planck Institute for Intelligent Systems, Germany

    3 publications · 504 citations

    3

    h-index

  5. SS
    Seth Sullivant

    North Carolina State University, United States

    2 publications · 90 citations

    2

    h-index

1–5 of 63

Venues

  1. arXiv (Cornell University)

    Repository · 5 publications · 262 citations

    5

    h-index

  2. eScholarship (California Digital Library)

    Repository · 3 publications · 540 citations

    3

    h-index

  3. TU/e Research Portal (Eindhoven University of Technology)

    Repository · 2 publications · 63 citations

    2

    h-index

  4. Bernoulli

    2 publications · 30 citations

    2

    h-index

  5. The Annals of Statistics

    2 publications · 97 citations

    2

    h-index

1–5