Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Consistency of common spatial estimators under spatial confounding.

Biometrika·2025
Same author

Incorrect statistical reasoning in Guyll et al. leads to biased claims about strength of forensic evidence.

Proceedings of the National Academy of Sciences of the United States of America·2024
Same author

Sensitivity analysis for principal ignorability violation in estimating complier and noncomplier average causal effects.

Statistics in medicine·2024
Same author

Causal Inference for Social Network Data.

Journal of the American Statistical Association·2024
Same author

Clarifying causal mediation analysis: Effect identification via three assumptions and five potential outcomes.

Journal of causal inference·2024
Same author

Causal mediation analysis: From simple to more robust strategies for estimation of marginal natural (in)direct effects.

Statistics surveys·2024

Related Experiment Video

Updated: Apr 20, 2026

Precision Implementation of Minimal Erythema Dose MED Testing to Assess Individual Variation in Human Inflammatory Response
06:31

Precision Implementation of Minimal Erythema Dose MED Testing to Assess Individual Variation in Human Inflammatory Response

Published on: October 3, 2019

9.3K

Commentary on "Mediation analysis without sequential ignorability: Using baseline covariates interacted with random

Elizabeth L Ogburn

    Journal of Statistical Research
    |December 2, 2014
    PubMed
    Summary

    This commentary discusses causal mediation analysis, evaluating the assumptions of a new model proposed by Dr. Small. It explores various mediation analysis schools and situates Small's work within the field.

    More Related Videos

    Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
    05:47

    Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

    Published on: June 13, 2025

    1.9K
    Ocular Therapeutic Delivery and Advanced Tissue Retrieval in Adult Rats
    06:30

    Ocular Therapeutic Delivery and Advanced Tissue Retrieval in Adult Rats

    Published on: May 23, 2025

    1.2K

    Related Experiment Videos

    Last Updated: Apr 20, 2026

    Precision Implementation of Minimal Erythema Dose MED Testing to Assess Individual Variation in Human Inflammatory Response
    06:31

    Precision Implementation of Minimal Erythema Dose MED Testing to Assess Individual Variation in Human Inflammatory Response

    Published on: October 3, 2019

    9.3K
    Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
    05:47

    Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

    Published on: June 13, 2025

    1.9K
    Ocular Therapeutic Delivery and Advanced Tissue Retrieval in Adult Rats
    06:30

    Ocular Therapeutic Delivery and Advanced Tissue Retrieval in Adult Rats

    Published on: May 23, 2025

    1.2K

    Area of Science:

    • Statistics
    • Causal Inference

    Background:

    • Causal mediation analysis is crucial for understanding indirect effects.
    • Existing models rely on specific assumptions that may limit their applicability.

    Purpose of the Study:

    • To critically evaluate the assumptions of a novel causal mediation model.
    • To contextualize the proposed estimand within the broader landscape of mediation analysis.

    Main Methods:

    • Review of different schools of mediation analysis.
    • Assessment of the assumptions underpinning a previously proposed causal mediation model.

    Main Results:

    • Discussion of the strengths and limitations of Small's proposed model.
    • Identification of key assumptions for robust causal mediation analysis.

    Conclusions:

    • Small's work advances causal mediation analysis by testing critical assumptions.
    • Further examination of assumptions is necessary for reliable indirect effect estimation.