G-estimation

G-Estimation is a statistical estimation method within the framework of marginal structural models that uses a structural nested mean model to estimate the causal effect of time-varying treatments by solving estimating equations derived from the assumption of no unmeasured confounding.

90

Publications

6.3K

Citations

181

Authors

125

Institutions

Publications per year

2017–2026

28

Variants

G-estimation Of Structural Nested Models

Authors

181

  1. JM
    James M. Robins

    Harvard University, United States

    9 publications · 910 citations

    9

    h-index

  2. MJ
    Mark J. van der Laan

    University of California, Berkeley, United States

    6 publications · 413 citations

    6

    h-index

  3. EE
    Erica E. M. Moodie

    McGill University, Canada

    6 publications · 284 citations

    6

    h-index

  4. JG
    Jessica G. Young

    Harvard University, United States

    5 publications · 299 citations

    5

    h-index

  5. MA
    Miguel A. Hernán

    Harvard University, United States

    5 publications · 449 citations

    5

    h-index

1–5 of 181

Venues

  1. Statistics in Medicine

    10 publications · 791 citations

    10

    h-index

  2. Biometrics

    6 publications · 310 citations

    6

    h-index

  3. The International Journal of Biostatistics

    5 publications · 446 citations

    5

    h-index

  4. Biometrika

    3 publications · 42 citations

    4

    h-index

  5. 4

    h-index

1–5