Back-door Criterion

The Back-Door Criterion is a formal condition in causal graphical models that identifies a set of covariates which, when adjusted for, suffices to eliminate confounding bias by blocking all back-door paths from the treatment variable to the outcome variable while not introducing new spurious associations.

25

Publications

1.1K

Citations

135

Authors

55

Institutions

Publications per year

2017–2026

5

Authors

135

  1. EB
    Elias Bareinboim

    Purdue University West Lafayette, United States

    3 publications · 149 citations

    3

    h-index

  2. JT
    Jin Tian

    Iowa State University, United States

    3 publications · 149 citations

    3

    h-index

  3. JP
    Judea Pearl

    University of California, Los Angeles, United States

    2 publications · 126 citations

    2

    h-index

  4. MK
    Manabu Kuroki

    Fukuoka University, Japan

    2 publications · 43 citations

    2

    h-index

  5. YJ
    Yonghan Jung

    Purdue University West Lafayette, United States

    2 publications · 57 citations

    2

    h-index

1–5 of 135

Venues

  1. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 3 publications · 149 citations

    3

    h-index

  2. arXiv (Cornell University)

    Repository · 1 publications · 20 citations

    1

    h-index

  3. Past & Present

    1 publications · 11 citations

    1

    h-index

  4. Review of Quantitative Finance and Accounting

    1 publications · 16 citations

    1

    h-index

  5. CentAUR (University of Reading)

    Repository · 1 publications · 13 citations

    1

    h-index

1–5