Principal Stratification

Principal Stratification is a causal inference framework that classifies units into distinct strata based on their potential outcomes under different treatment conditions, particularly regarding post-treatment variables, thereby enabling the definition and estimation of causal effects within these strata to address issues such as non-compliance, censoring, or treatment effect heterogeneity.

118

Publications

9.1K

Citations

208

Authors

150

Institutions

Publications per year

2017–2026

24

Authors

208

  1. FM
    Fabrizia Mealli

    University of Florence, Italy

    15 publications · 714 citations

    14

    h-index

  2. DB
    Donald B. Rubin

    Harvard University, United States

    13 publications · 4K citations

    12

    h-index

  3. AM
    Alessandra Mattei

    University of Florence, Italy

    7 publications · 300 citations

    7

    h-index

  4. PD
    Peng Ding

    University of California, Berkeley, United States

    7 publications · 335 citations

    7

    h-index

  5. FL
    Fan Li

    Yale University, United States

    6 publications · 236 citations

    6

    h-index

1–5 of 208

Venues

  1. Journal of the American Statistical Association

    15 publications · 1.1K citations

    13

    h-index

  2. Statistics in Medicine

    12 publications · 622 citations

    12

    h-index

  3. Biometrics

    11 publications · 2.3K citations

    11

    h-index

  4. Biostatistics

    7 publications · 266 citations

    7

    h-index

  5. The Annals of Applied Statistics

    AOAS · 7 publications · 1.1K citations

    7

    h-index

1–5