Markov Equivalence

Mark Equivalence is a theoretical construct in the domain of causal graphical models that designates a set of directed acyclic graphs which encode the same conditional independence relations among variables, thereby being indistinguishable based on observational data alone.

80

Publications

4K

Citations

126

Authors

69

Institutions

Publications per year

2017–2026

28

Variants

Markov Equivalence Class

Authors

126

  1. MD
    Michael D. Perlman

    University of Washington, United States

    7 publications · 965 citations

    7

    h-index

  2. CU
    Caroline Uhler

    Massachusetts Institute of Technology, United States

    6 publications · 97 citations

    6

    h-index

  3. SA
    Steen A. Andersson

    University of Washington, United States

    6 publications · 893 citations

    6

    h-index

  4. FC
    Federico Castelletti

    Università Cattolica del Sacro Cuore, Italy

    5 publications · 94 citations

    5

    h-index

  5. PB
    Peter Bühlmann

    ETH Zurich, Switzerland

    5 publications · 525 citations

    5

    h-index

1–5 of 126

Venues

  1. arXiv (Cornell University)

    Repository · 15 publications · 628 citations

    12

    h-index

  2. The Annals of Statistics

    6 publications · 636 citations

    6

    h-index

  3. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 4 publications · 75 citations

    4

    h-index

  4. Scandinavian Journal of Statistics

    4 publications · 338 citations

    4

    h-index

  5. DSpace@MIT (Massachusetts Institute of Technology)

    Repository · 2 publications · 23 citations

    2

    h-index

1–5