Dynamic Hypothesis Generation

Dynamic Hypothesis Generation is a systematic methodological approach within causal inference that enables the iterative formulation and refinement of causal hypotheses through feedback-rich modeling environments, distinguishing itself from static hypothesis formulation by explicitly incorporating temporal and systemic interactions in complex adaptive systems.

16

Publications

497

Citations

43

Authors

24

Institutions

Publications per year

2017–2026

3

Authors

43

  1. RP
    Rick P. Thomas

    University of Oklahoma, United States

    2 publications · 30 citations

    2

    h-index

  2. EJ
    Eddy J. Davelaar

    Birkbeck, University of London, United Kingdom

    2 publications · 30 citations

    2

    h-index

  3. ND
    Nicholas D. Lange

    Birkbeck, University of London, United Kingdom

    2 publications · 30 citations

    2

    h-index

  4. DB
    Daniel B. Carr

    Tufts Medical Center, United States

    2 publications · 76 citations

    2

    h-index

  5. RT
    Roger T. Hartley

    University of Leeds, United Kingdom

    1 publications · 33 citations

    1

    h-index

1–5 of 43

Venues

  1. 1

    h-index

  2. Neurocomputing

    1 publications · 55 citations

    1

    h-index

  3. Wound Repair and Regeneration

    1 publications · 23 citations

    1

    h-index

  4. System Dynamics Review

    1 publications · 17 citations

    1

    h-index

  5. 1

    h-index

1–5