Bias Control

Bias Control is a set of methodological strategies in experimental design that systematically prevent, detect, and adjust for nonrandom sources of error in the selection, allocation, measurement, or treatment of study units, thereby minimizing systematic bias and protecting the validity of causal inferences.

3.5K

Publications

536K

Citations

7.6K

Authors

2.2K

Institutions

Publications per year

2017–2026

1.1K

Authors

7.6K

  1. PR
    Paul R. Rosenbaum

    University of Pennsylvania, United States

    30 publications · 2.3K citations

    26

    h-index

  2. SG
    Sander Greenland

    University of California, Los Angeles, United States

    18 publications · 4.1K citations

    17

    h-index

  3. TJ
    Tyler J. VanderWeele

    Harvard University, United States

    18 publications · 2.5K citations

    15

    h-index

  4. DB
    Donald B. Rubin

    Harvard University, United States

    16 publications · 11.7K citations

    15

    h-index

  5. DS
    Dylan S. Small

    University of Pennsylvania, United States

    16 publications · 811 citations

    15

    h-index

1–5 of 7.6K

Venues

  1. Statistics in Medicine

    111 publications · 13.4K citations

    46

    h-index

  2. Journal of the American Statistical Association

    64 publications · 12.4K citations

    37

    h-index

  3. American Journal of Epidemiology

    AJE · 53 publications · 12.5K citations

    37

    h-index

  4. Journal of Clinical Epidemiology

    57 publications · 7.2K citations

    36

    h-index

  5. SSRN Electronic Journal

    Repository · 79 publications · 7.8K citations

    35

    h-index

1–5