Meta-learners

Meta-Learners is a class of algorithmic frameworks in causal inference that decomposes the estimation of heterogeneous treatment effects into separate, reweighted machine learning tasks to isolate treatment-specific response patterns from observational data.

141

Publications

8.2K

Citations

538

Authors

264

Institutions

Publications per year

2017–2026

110

Authors

538

  1. CS
    Carlos Soares

    Universidade do Porto, Portugal

    4 publications · 230 citations

    4

    h-index

  2. AC
    André C. P. L. F. de Carvalho

    Universidade de São Paulo, Brazil

    4 publications · 148 citations

    4

    h-index

  3. JN
    Jing Nie

    Shihezi University, China

    2 publications · 55 citations

    2

    h-index

  4. AD
    AnHai Doan

    University of Wisconsin–Madison, United States

    2 publications · 872 citations

    2

    h-index

  5. AB
    Andrea Bondavalli

    University of Florence, Italy

    2 publications · 112 citations

    2

    h-index

1–5 of 538

Venues

  1. arXiv (Cornell University)

    Repository · 16 publications · 1.4K citations

    13

    h-index

  2. IEEE Access

    4 publications · 632 citations

    4

    h-index

  3. Neurocomputing

    5 publications · 440 citations

    4

    h-index

  4. Mechanical Systems and Signal Processing

    MSSP · 3 publications · 415 citations

    3

    h-index

  5. 2

    h-index

1–5