Overlap Assumption

The Overlap Assumption is a foundational condition in propensity score methods that requires every unit in a study sample to have a nonzero probability of being assigned to either treatment or control, ensuring that the distributions of covariates in the treated and control groups have common support, thereby enabling valid causal comparisons.

69

Publications

5.3K

Citations

147

Authors

98

Institutions

Publications per year

2017–2026

18

Authors

147

  1. PD
    Peng Ding

    University of California, Berkeley, United States

    3 publications · 220 citations

    3

    h-index

  2. MB
    Mark Britten‐Jones

    London Business School, United Kingdom

    2 publications · 81 citations

    2

    h-index

  3. AM
    Alessandra Mattei

    University of Florence, Italy

    2 publications · 72 citations

    2

    h-index

  4. FL
    Fan Li

    Yale University, United States

    2 publications · 195 citations

    2

    h-index

  5. AN
    Anthony Neuberger

    London Business School, United Kingdom

    2 publications · 81 citations

    2

    h-index

1–5 of 147

Venues

  1. arXiv (Cornell University)

    Repository · 4 publications · 94 citations

    4

    h-index

  2. Computational Statistics & Data Analysis

    CSDA · 3 publications · 279 citations

    3

    h-index

  3. Journal of the Atmospheric Sciences

    2 publications · 37 citations

    2

    h-index

  4. 2

    h-index

  5. SSRN Electronic Journal

    Repository · 2 publications · 70 citations

    2

    h-index

1–5