Bayesian Causal Effect Estimation

Bayesian Causal Effect Estimation is a statistical framework that uses Bayesian inference to estimate causal effects by incorporating prior knowledge and modeling uncertainty in both observed data and unobserved confounding structures.

217

Publications

14.6K

Citations

638

Authors

338

Institutions

Publications per year

2017–2026

97

Authors

638

  1. MJ
    Michael J. Daniels

    University of Florida, United States

    9 publications · 244 citations

    9

    h-index

  2. CZ
    Corwin Zigler

    Harvard University, United States

    7 publications · 377 citations

    7

    h-index

  3. PG
    Paul Gustafson

    University of British Columbia, Canada

    6 publications · 215 citations

    6

    h-index

  4. FL
    Fan Li

    Yale University, United States

    5 publications · 141 citations

    5

    h-index

  5. FD
    Francesca Dominici

    Harvard University, United States

    5 publications · 396 citations

    5

    h-index

1–5 of 638

Venues

  1. Biometrics

    13 publications · 635 citations

    13

    h-index

  2. Statistics in Medicine

    14 publications · 517 citations

    12

    h-index

  3. The Annals of Applied Statistics

    AOAS · 9 publications · 1.2K citations

    9

    h-index

  4. Biostatistics

    7 publications · 244 citations

    7

    h-index

  5. arXiv (Cornell University)

    Repository · 7 publications · 109 citations

    7

    h-index

1–5