Dynamic Marginal Structural Models

Dynamic Marginal Structural Models is a causal inference framework that extends marginal structural models to estimate the effects of time-varying treatments by accounting for time-dependent confounders through the specification of dynamic treatment regimes, thereby enabling the evaluation of policies in which treatment decisions depend adaptively on the evolving history of covariates and prior treatments.

24

Publications

1.6K

Citations

94

Authors

70

Institutions

Publications per year

2017–2026

7

Authors

94

  1. JM
    James M. Robins

    Harvard University, United States

    4 publications · 320 citations

    4

    h-index

  2. AR
    Andrea Rotnitzky

    Harvard University, United States

    3 publications · 270 citations

    3

    h-index

  3. EE
    Erica E. M. Moodie

    McGill University, Canada

    2 publications · 58 citations

    2

    h-index

  4. MJ
    Mark J. van der Laan

    University of California, Berkeley, United States

    2 publications · 111 citations

    2

    h-index

  5. RN
    Romain Neugebauer

    Kaiser Permanente, United States

    2 publications · 129 citations

    2

    h-index

1–5 of 94

Venues

  1. Statistics in Medicine

    3 publications · 123 citations

    3

    h-index

  2. Biometrics

    3 publications · 67 citations

    3

    h-index

  3. The International Journal of Biostatistics

    2 publications · 258 citations

    2

    h-index

  4. American Journal of Epidemiology

    AJE · 1 publications · 18 citations

    1

    h-index

  5. Biostatistics

    1 publications · 14 citations

    1

    h-index

1–5