Inference Under Randomization

Inference Under Randomization is a statistical framework that enables causal conclusions by relying on the random assignment of treatments as the sole basis for deriving valid probabilistic assessments of treatment effects.

260

Publications

22.3K

Citations

460

Authors

266

Institutions

Publications per year

2017–2026

59

Variants

Randomization-based Inference

Authors

460

  1. DS
    Dylan S. Small

    University of Pennsylvania, United States

    6 publications · 212 citations

    6

    h-index

  2. PD
    Peng Ding

    University of California, Berkeley, United States

    5 publications · 269 citations

    5

    h-index

  3. JJ
    James J. Heckman

    University of Chicago, United States

    4 publications · 189 citations

    4

    h-index

  4. SS
    Sarjinder Singh

    Texas A&M University – Kingsville, United States

    4 publications · 101 citations

    4

    h-index

  5. AM
    Azeem M. Shaikh

    University of Chicago, United States

    4 publications · 227 citations

    4

    h-index

1–5 of 460

Venues

  1. Journal of the American Statistical Association

    25 publications · 1.6K citations

    19

    h-index

  2. Statistics in Medicine

    12 publications · 821 citations

    12

    h-index

  3. Biometrika

    8 publications · 1.8K citations

    8

    h-index

  4. Psychological Methods

    8 publications · 576 citations

    8

    h-index

  5. SSRN Electronic Journal

    Repository · 7 publications · 209 citations

    7

    h-index

1–5