Sequential Causal Inference

Sequential Causal Inference is a longitudinal causal inference framework that specifically addresses the identification and estimation of causal effects in settings where treatments, confounders, and outcomes evolve over time in a temporal sequence, distinguishing itself by modeling the dynamic, time-varying nature of interventions and their cumulative impacts on outcomes.

22

Publications

887

Citations

66

Authors

43

Institutions

Publications per year

2017–2026

6

Variants

Dynamic Causal Inference

Authors

66

  1. RD
    Ree Dawson

    Frontier Science & Technology Research Foundation, Inc., United States

    2 publications · 36 citations

    2

    h-index

  2. PW
    Philip W. Lavori

    Stanford University, United States

    2 publications · 36 citations

    2

    h-index

  3. GV
    Gregory Veramendi

    University of Chicago, United States

    1 publications · 58 citations

    1

    h-index

  4. JE
    John Eric Humphries

    University of Virginia, United States

    1 publications · 58 citations

    1

    h-index

  5. NR
    Neil R Bramley

    University of Edinburgh, United Kingdom

    1 publications · 11 citations

    1

    h-index

1–5 of 66

Venues

  1. Statistics in Medicine

    3 publications · 124 citations

    3

    h-index

  2. arXiv (Cornell University)

    Repository · 2 publications · 46 citations

    2

    h-index

  3. UCL Discovery (University College London)

    Repository · 1 publications · 11 citations

    1

    h-index

  4. Argumentation and Advocacy

    1 publications · 12 citations

    1

    h-index

  5. RePEc: Research Papers in Economics

    Repository · 1 publications · 58 citations

    1

    h-index

1–5