Causal Feature Selection

Causal Feature Selection is a computational methodology within multi-omics causal inference that identifies subsets of variables exhibiting causal relationships with an outcome of interest, distinguishing itself from associative or predictive feature selection by explicitly inferring directionality and causal mechanisms from observational or experimental multi-omics data.

52

Publications

2.8K

Citations

161

Authors

80

Institutions

Publications per year

2017–2026

35

Authors

161

  1. KY
    Kui Yu

    Hefei University of Technology, China

    12 publications · 572 citations

    12

    h-index

  2. JL
    Jiuyong Li

    University of South Australia, Australia

    7 publications · 399 citations

    7

    h-index

  3. LL
    Lin Liu

    University of South Australia, Australia

    6 publications · 307 citations

    6

    h-index

  4. XW
    Xingyu Wu

    University of Science and Technology of China, China

    4 publications · 217 citations

    4

    h-index

  5. HC
    Huanhuan Chen

    University of Science and Technology of China, China

    3 publications · 178 citations

    3

    h-index

1–5 of 161

Venues

  1. arXiv (Cornell University)

    Repository · 4 publications · 89 citations

    4

    h-index

  2. 3

    h-index

  3. Information Sciences

    2 publications · 65 citations

    2

    h-index

  4. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 2 publications · 68 citations

    2

    h-index

  5. Journal of Machine Learning Research

    JMLR · 2 publications · 217 citations

    2

    h-index

1–5