Causal Estimation

Causal Estimation is a methodological framework within the Potential Outcomes Framework that identifies and quantifies the causal effect of a treatment or intervention by comparing observed outcomes under different hypothetical treatment assignments, distinguishing it from predictive or associative methods through its focus on counterfactual reasoning and unbiased effect measurement.

1.1K

Publications

148.4K

Citations

2.2K

Authors

914

Institutions

Publications per year

2017–2026

462

Authors

2.2K

  1. DB
    Donald B. Rubin

    Harvard University, United States

    23 publications · 23.6K citations

    23

    h-index

  2. MA
    Miguel A. Hernán

    Harvard University, United States

    26 publications · 8.8K citations

    22

    h-index

  3. JP
    Judea Pearl

    University of California, Los Angeles, United States

    28 publications · 5.2K citations

    21

    h-index

  4. MJ
    Mark J. van der Laan

    University of California, Berkeley, United States

    29 publications · 1.7K citations

    20

    h-index

  5. JM
    James M. Robins

    Harvard University, United States

    21 publications · 12.3K citations

    17

    h-index

1–5 of 2.2K

Venues

  1. Journal of the American Statistical Association

    44 publications · 22.5K citations

    31

    h-index

  2. arXiv (Cornell University)

    Repository · 69 publications · 2.3K citations

    28

    h-index

  3. Statistics in Medicine

    31 publications · 4K citations

    24

    h-index

  4. American Journal of Epidemiology

    AJE · 32 publications · 7K citations

    24

    h-index

  5. Epidemiology

    27 publications · 12.3K citations

    22

    h-index

1–5