Marginal Structural Models

Marginal Structural Models is a class of causal statistical models that represent the marginal (population-level) counterfactual mean of an outcome as a function of treatment or exposure history and are typically estimated via inverse-probability weighting to identify causal effects in the presence of time-varying confounding affected by prior exposure.

270

Publications

27.5K

Citations

708

Authors

389

Institutions

Publications per year

2017–2026

77

Authors

708

  1. MJ
    Mark J. van der Laan

    University of California, Berkeley, United States

    26 publications · 2.1K citations

    19

    h-index

  2. EE
    Erica E. M. Moodie

    McGill University, Canada

    15 publications · 826 citations

    14

    h-index

  3. JM
    James M. Robins

    Harvard University, United States

    14 publications · 9.3K citations

    13

    h-index

  4. SV
    Stijn Vansteelandt

    Ghent University, Belgium

    12 publications · 849 citations

    12

    h-index

  5. MA
    Miguel A. Hernán

    Harvard University, United States

    11 publications · 11.7K citations

    11

    h-index

1–5 of 708

Venues

  1. Statistics in Medicine

    35 publications · 2.2K citations

    23

    h-index

  2. American Journal of Epidemiology

    AJE · 26 publications · 5K citations

    21

    h-index

  3. Epidemiology

    15 publications · 9.5K citations

    15

    h-index

  4. The International Journal of Biostatistics

    16 publications · 1.2K citations

    14

    h-index

  5. Journal of the American Statistical Association

    12 publications · 1.2K citations

    11

    h-index

1–5