Missing Data In Causal Analysis

Missing Data in Causal Analysis is a methodological challenge in statistical inference where the absence of certain values in observed or potential outcomes undermines the validity of causal effect estimation under assumptions such as ignorability, distinguishable by its direct impact on the identifiability and bias of causal parameters.

356

Publications

29.5K

Citations

838

Authors

428

Institutions

Publications per year

2017–2026

120

Authors

838

  1. DB
    Donald B. Rubin

    Harvard University, United States

    12 publications · 3.2K citations

    12

    h-index

  2. JP
    Judea Pearl

    University of California, Los Angeles, United States

    6 publications · 287 citations

    6

    h-index

  3. MA
    Michael A. Gottfried

    University of California, Santa Barbara, United States

    5 publications · 670 citations

    5

    h-index

  4. SV
    Stijn Vansteelandt

    Ghent University, Belgium

    5 publications · 324 citations

    5

    h-index

  5. FM
    Fabrizia Mealli

    University of Florence, Italy

    5 publications · 283 citations

    5

    h-index

1–5 of 838

Venues

  1. Statistics in Medicine

    19 publications · 675 citations

    15

    h-index

  2. Biometrics

    14 publications · 2.2K citations

    13

    h-index

  3. arXiv (Cornell University)

    Repository · 12 publications · 261 citations

    11

    h-index

  4. Journal of the American Statistical Association

    10 publications · 1.1K citations

    10

    h-index

  5. American Journal of Epidemiology

    AJE · 8 publications · 464 citations

    8

    h-index

1–5