Statistical Causality Assessment

Statistical Causality Assessment is a formal inferential framework within genetic fine-mapping that distinguishes causal variants from non-causal associations by integrating statistical evidence of association with biological priors and probabilistic models to quantify the likelihood of causality at a given genomic locus.

22

Publications

1.2K

Citations

48

Authors

37

Institutions

Publications per year

2017–2026

8

Authors

48

  1. AP
    A. P. Dawid

    University College London, United Kingdom

    2 publications · 69 citations

    2

    h-index

  2. LP
    Ljiljana Petrović

    University of Belgrade, Serbia

    2 publications · 36 citations

    2

    h-index

  3. AA
    Akihiro Amano

    Kobe University, Japan

    2 publications · 366 citations

    2

    h-index

  4. YN
    Yuriko Nakao

    Kwansei Gakuin University, Japan

    2 publications · 366 citations

    2

    h-index

  5. MN
    Makiko Nakano

    Keio University, Japan

    2 publications · 366 citations

    2

    h-index

1–5 of 48

Venues

  1. Journal of Conflict Resolution

    2 publications · 139 citations

    2

    h-index

  2. 2

    h-index

  3. Economic Record

    2 publications · 137 citations

    2

    h-index

  4. Strategic Management Journal

    SMJ · 1 publications · 82 citations

    1

    h-index

  5. Journal of Transport and Supply Chain Management

    JTSCM · 1 publications · 20 citations

    1

    h-index

1–5