Conditional Independence

Conditional Independence is a probabilistic relationship in which the occurrence of one event does not influence the probability of another event given the knowledge of a third event, formalized within the structure of a Directed Acyclic Graph through d-separation.

1.3K

Publications

85K

Citations

2.3K

Authors

907

Institutions

Publications per year

2017–2026

278

Authors

2.3K

  1. PS
    Peter Spirtes

    Carnegie Mellon University, United States

    11 publications · 649 citations

    11

    h-index

  2. JP
    Judea Pearl

    University of California, Los Angeles, United States

    10 publications · 1.1K citations

    10

    h-index

  3. MS
    Milan Studený

    Czech Academy of Sciences, Institute of Information Theory and Automation, Czechia

    11 publications · 311 citations

    10

    h-index

  4. DJ
    Dominik Janzing

    Max Planck Institute for Intelligent Systems, Germany

    10 publications · 809 citations

    10

    h-index

  5. TS
    Thomas S. Richardson

    University of Washington, United States

    9 publications · 614 citations

    9

    h-index

1–5 of 2.3K

Venues

  1. arXiv (Cornell University)

    Repository · 110 publications · 5.1K citations

    35

    h-index

  2. Journal of the American Statistical Association

    55 publications · 5.2K citations

    31

    h-index

  3. The Annals of Statistics

    39 publications · 5.9K citations

    30

    h-index

  4. Biometrika

    24 publications · 1.1K citations

    17

    h-index

  5. Biometrics

    22 publications · 1.7K citations

    16

    h-index

1–5