Directed Acyclic Graph Learning

Directed Acyclic Graph Learning is a computational method in causal discovery that identifies the structure of a directed acyclic graph from observational data by inferring causal relationships among variables while enforcing acyclicity constraints.

296

Publications

18.3K

Citations

791

Authors

339

Institutions

Publications per year

2017–2026

150

Authors

791

  1. CU
    Caroline Uhler

    Massachusetts Institute of Technology, United States

    7 publications · 185 citations

    7

    h-index

  2. PS
    Peter Spirtes

    Carnegie Mellon University, United States

    6 publications · 1.2K citations

    6

    h-index

  3. SZ
    Shengyu Zhu

    Huawei Technologies (China), China

    6 publications · 257 citations

    6

    h-index

  4. TC
    Tat‐Seng Chua

    National University of Singapore, Singapore

    5 publications · 378 citations

    5

    h-index

  5. MG
    Marco Gori

    University of Siena, Italy

    5 publications · 574 citations

    5

    h-index

1–5 of 791

Venues

  1. arXiv (Cornell University)

    Repository · 52 publications · 1.8K citations

    25

    h-index

  2. Journal of Machine Learning Research

    JMLR · 6 publications · 715 citations

    6

    h-index

  3. Neural Information Processing Systems

    Conference · 5 publications · 215 citations

    5

    h-index

  4. IEEE Transactions on Neural Networks

    5 publications · 1.8K citations

    5

    h-index

  5. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 5 publications · 353 citations

    4

    h-index

1–5