Score-based Causal Discovery

Score-Based Causal Discovery is a method for estimating causal graphical models by assigning a score to each candidate graph based on how well it fits the observational data while penalizing complexity, and then selecting the graph with the optimal score.

24

Publications

935

Citations

74

Authors

36

Institutions

Publications per year

2017–2026

15

Variants

Score-based Structure Learning

Authors

74

  1. PS
    Peter Spirtes

    Carnegie Mellon University, United States

    3 publications · 70 citations

    3

    h-index

  2. SM
    Subramani Mani

    Vanderbilt University, United States

    2 publications · 49 citations

    2

    h-index

  3. BS
    Basil Saeed

    Massachusetts Institute of Technology, United States

    2 publications · 22 citations

    2

    h-index

  4. SZ
    Shengyu Zhu

    Huawei Technologies (China), China

    2 publications · 118 citations

    2

    h-index

  5. DI
    Daniel Irving Bernstein

    North Carolina State University, United States

    2 publications · 22 citations

    2

    h-index

1–5 of 74

Venues

  1. arXiv (Cornell University)

    Repository · 8 publications · 237 citations

    8

    h-index

  2. Annals of the New York Academy of Sciences

    1 publications · 14 citations

    1

    h-index

  3. Behaviormetrika

    1 publications · 17 citations

    1

    h-index

  4. 1

    h-index

  5. Journal of Artificial Intelligence Research

    JAIR · 1 publications · 121 citations

    1

    h-index

1–5