High-dimensional Heterogeneous Effects

High-Dimensional Heterogeneous Effects is a statistical framework in causal inference that characterizes the variation in treatment effects across a large number of covariates or subgroups, where the dimensionality of effect modifiers approaches or exceeds the sample size, necessitating regularization or dimensionality reduction techniques to identify structured heterogeneity.

27

Publications

1.1K

Citations

81

Authors

56

Institutions

Publications per year

2017–2026

18

Authors

81

  1. VC
    Victor Chernozhukov

    Massachusetts Institute of Technology, United States

    4 publications · 227 citations

    4

    h-index

  2. IF
    Iván Fernández‐Val

    Boston University, United States

    3 publications · 203 citations

    3

    h-index

  3. JQ
    Junyang Qian

    Stanford University, United States

    2 publications · 221 citations

    2

    h-index

  4. CH
    Christian Hansen

    University of Chicago, United States

    2 publications · 68 citations

    2

    h-index

  5. MD
    Mert Demirer

    Massachusetts Institute of Technology, United States

    2 publications · 159 citations

    2

    h-index

1–5 of 81

Venues

  1. arXiv (Cornell University)

    Repository · 5 publications · 110 citations

    5

    h-index

  2. SSRN Electronic Journal

    Repository · 3 publications · 62 citations

    3

    h-index

  3. Statistics in Medicine

    1 publications · 29 citations

    1

    h-index

  4. reroDoc Digital Library

    Repository · 1 publications · 20 citations

    1

    h-index

  5. Numerische Mathematik

    1 publications · 23 citations

    1

    h-index

1–5