Functional Causal Model Discovery

Functional Causal Model Discovery is a methodological framework within causal discovery that identifies causal relationships by modeling each variable as a deterministic function of its direct causes and an independent noise term, distinguishing itself from purely probabilistic or constraint-based approaches through its explicit functional form assumptions.

9

Publications

433

Citations

43

Authors

15

Institutions

Publications per year

2017–2026

3

Authors

43

  1. KZ
    Kun Zhang

    Carnegie Mellon University, United States

    2 publications · 62 citations

    2

    h-index

  2. ER
    Eytan Ruppin

    Tel Aviv University, Israel

    1 publications · 34 citations

    1

    h-index

  3. BA
    B.J.M. Ale

    Delft University of Technology, Netherlands

    1 publications · 27 citations

    1

    h-index

  4. ZZ
    Zhenjie Zhang

    Hangzhou Dianzi University, China

    1 publications · 20 citations

    1

    h-index

  5. JQ
    Jie Qiao

    Guangdong University of Technology, China

    1 publications · 20 citations

    1

    h-index

1–5 of 43

Venues

  1. arXiv (Cornell University)

    Repository · 1 publications · 64 citations

    1

    h-index

  2. PLoS Computational Biology

    1 publications · 34 citations

    1

    h-index

  3. 1

    h-index

  4. 1

    h-index

  5. American Journal of Sociology

    AJS · 1 publications · 165 citations

    1

    h-index

1–5