Causal Sensitivity Analysis

Causal Sensitivity Analysis is a methodological framework within multi-omics causal inference that systematically evaluates the robustness of inferred causal relationships to unmeasured confounding, selection bias, or model misspecification by quantifying how inferences change under plausible deviations from key causal assumptions.

146

Publications

17.7K

Citations

359

Authors

207

Institutions

Publications per year

2017–2026

79

Authors

359

  1. TJ
    Tyler J. VanderWeele

    Harvard University, United States

    10 publications · 2.2K citations

    10

    h-index

  2. DS
    Dylan S. Small

    University of Pennsylvania, United States

    4 publications · 155 citations

    4

    h-index

  3. LK
    Luke Keele

    University of Pennsylvania, United States

    4 publications · 4K citations

    4

    h-index

  4. DT
    Dustin Tingley

    Harvard University Press, United States

    4 publications · 4K citations

    4

    h-index

  5. MB
    Maya B. Mathur

    Stanford University, United States

    4 publications · 332 citations

    4

    h-index

1–5 of 359

Venues

  1. Statistics in Medicine

    6 publications · 276 citations

    6

    h-index

  2. arXiv (Cornell University)

    Repository · 4 publications · 99 citations

    4

    h-index

  3. 4

    h-index

  4. SSRN Electronic Journal

    Repository · 4 publications · 383 citations

    4

    h-index

  5. Psychological Methods

    4 publications · 3.9K citations

    4

    h-index

1–5