Conditional Average Treatment Effect Estimation

Conditional Average Treatment Effect Estimation is a causal inference method that identifies the expected treatment effect for a unit conditional on its specific covariate values, distinguishing itself from average treatment effect estimation by enabling heterogeneous effect assessment across subpopulations defined by observed characteristics.

319

Publications

31.7K

Citations

833

Authors

454

Institutions

Publications per year

2017–2026

166

Variants

Cate Estimation

Authors

833

  1. GW
    Guido W. Imbens

    Stanford University, United States

    16 publications · 6.5K citations

    14

    h-index

  2. JJ
    James J. Heckman

    University of Chicago, United States

    9 publications · 1.5K citations

    9

    h-index

  3. VC
    Victor Chernozhukov

    Massachusetts Institute of Technology, United States

    7 publications · 499 citations

    7

    h-index

  4. EA
    Elizabeth A. Stuart

    Johns Hopkins University, United States

    7 publications · 643 citations

    7

    h-index

  5. SA
    Susan Athey

    Stanford University, United States

    6 publications · 1.6K citations

    6

    h-index

1–5 of 833

Venues

  1. arXiv (Cornell University)

    Repository · 24 publications · 609 citations

    16

    h-index

  2. Statistics in Medicine

    14 publications · 779 citations

    14

    h-index

  3. SSRN Electronic Journal

    Repository · 12 publications · 326 citations

    11

    h-index

  4. National Bureau of Economic Research

    Repository · 8 publications · 1.7K citations

    8

    h-index

  5. Journal of Business and Economic Statistics

    JBES · 8 publications · 2K citations

    8

    h-index

1–5