IDEAS home Printed from https://ideas.repec.org/a/sae/evarev/v37y2013i5p405-431.html
   My bibliography  Save this article

Sensitivity Plots for Confounder Bias in the Single Mediator Model

Author

Listed:
  • Matthew G. Cox
  • Yasemin Kisbu-Sakarya
  • Milica MioÄ ević
  • David P. MacKinnon

Abstract

Background: Causal inference continues to be a critical aspect of evaluation research. Recent research in causal inference for statistical mediation has focused on addressing the sequential ignorability assumption; specifically, that there is no confounding between the mediator and the outcome variable. Objectives: This article compares and contrasts three different methods for assessing sensitivity to confounding and describes the graphical depiction of these methods. Design: Two types of data were used to fully examine the plots for sensitivity analysis. The first type was generated data from a single mediator model with a confounder influencing both the mediator and the outcome variable. The second was data from an actual intervention study. With both types of data, situations are examined where confounding has a large effect and a small effect. Subjects: The nonsimulated data were from a large intervention study to decrease intentions to use steroids among high school football players. We demonstrate one situation where confounding is likely and another situation where confounding is unlikely. Conclusions: We discuss how these methods could be implemented in future mediation studies as well as the limitations and future directions for these methods.

Suggested Citation

  • Matthew G. Cox & Yasemin Kisbu-Sakarya & Milica MioÄ ević & David P. MacKinnon, 2013. "Sensitivity Plots for Confounder Bias in the Single Mediator Model," Evaluation Review, , vol. 37(5), pages 405-431, October.
    • Handle: RePEc:sae:evarev:v:37:y:2013:i:5:p:405-431
    DOI: 10.1177/0193841X14524576
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/0193841X14524576
    Download Restriction: no
    File URL: https://libkey.io/10.1177/0193841X14524576?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Imai, Kosuke & Yamamoto, Teppei, 2013. "Identification and Sensitivity Analysis for Multiple Causal Mechanisms: Revisiting Evidence from Framing Experiments," Political Analysis, Cambridge University Press, vol. 21(2), pages 141-171, April.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Acharya, Avidit & Blackwell, Matthew & Sen, Maya, 2016. "Explaining Causal Findings Without Bias: Detecting and Assessing Direct Effects," American Political Science Review, Cambridge University Press, vol. 110(3), pages 512-529, August.
    2. Parker Hevron, 2018. "Judicialization and Its Effects: Experiments as a Way Forward," Laws, MDPI, vol. 7(2), pages 1-21, May.
    3. Martin Huber & Yu‐Chin Hsu & Ying‐Ying Lee & Layal Lettry, 2020. "Direct and indirect effects of continuous treatments based on generalized propensity score weighting," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(7), pages 814-840, November.
    4. Martin Huber & Michael Lechner & Giovanni Mellace, 2017. "Why Do Tougher Caseworkers Increase Employment? The Role of Program Assignment as a Causal Mechanism," The Review of Economics and Statistics, MIT Press, vol. 99(1), pages 180-183, March.
    5. Martin Huber & Mark Schelker & Anthony Strittmatter, 2022. "Direct and Indirect Effects based on Changes-in-Changes," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(1), pages 432-443, January.
    6. Martin Huber & Michael Lechner & Giovanni Mellace, 2016. "The Finite Sample Performance of Estimators for Mediation Analysis Under Sequential Conditional Independence," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(1), pages 139-160, January.
    7. Xu Qin & Jonah Deutsch & Guanglei Hong, 2021. "Unpacking Complex Mediation Mechanisms And Their Heterogeneity Between Sites In A Job Corps Evaluation," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 40(1), pages 158-190, January.
    8. Schaub, Max, 2021. "Acute Financial Hardship and Voter Turnout: Theory and Evidence from the Sequence of Bank Working Days," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 115(4), pages 1258-1274.
    9. Guanglei Hong & Fan Yang & Xu Qin, 2023. "Posttreatment confounding in causal mediation studies: A cutting‐edge problem and a novel solution via sensitivity analysis," Biometrics, The International Biometric Society, vol. 79(2), pages 1042-1056, June.
    10. Zhao, Yi & Luo, Xi, 2023. "Multilevel mediation analysis with structured unmeasured mediator-outcome confounding," Computational Statistics & Data Analysis, Elsevier, vol. 179(C).
    11. Tingley, Dustin & Yamamoto, Teppei & Hirose, Kentaro & Keele, Luke & Imai, Kosuke, 2014. "mediation: R Package for Causal Mediation Analysis," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 59(i05).
    12. Li, Fanlue & He, Ke & Wang, Yuejie & Zhang, Junbiao, 2021. "Does Indoor Air Pollution from Solid Fuels Influence the Mental Health of Rural Residents? Evidence from China," 2021 Conference, August 17-31, 2021, Virtual 315024, International Association of Agricultural Economists.
    13. Nicholas Weller & Jeb Barnes, 2016. "Pathway Analysis and the Search for Causal Mechanisms," Sociological Methods & Research, , vol. 45(3), pages 424-457, August.
    14. Soojin Park & Peter M. Steiner & David Kaplan, 2018. "Identification and Sensitivity Analysis for Average Causal Mediation Effects with Time-Varying Treatments and Mediators: Investigating the Underlying Mechanisms of Kindergarten Retention Policy," Psychometrika, Springer;The Psychometric Society, vol. 83(2), pages 298-320, June.
    15. Park Soojin & Kürüm Esra, 2020. "A Two-Stage Joint Modeling Method for Causal Mediation Analysis in the Presence of Treatment Noncompliance," Journal of Causal Inference, De Gruyter, vol. 8(1), pages 131-149, January.
    16. Julie H Nielsen, 2018. "The effect of affect: How affective style determines attitudes towards the EU," European Union Politics, , vol. 19(1), pages 75-96, March.
    17. Adam C. Sales, 2017. "Review," Journal of Educational and Behavioral Statistics, , vol. 42(1), pages 69-84, February.
    18. Cammett, Melani & Şaşmaz, Aytuğ, 2017. "Political Context, Organizational Mission, and the Quality of Social Services: Insights from the Health Sector in Lebanon," World Development, Elsevier, vol. 98(C), pages 120-132.
    19. Jing Peng, 2023. "Identification of Causal Mechanisms from Randomized Experiments: A Framework for Endogenous Mediation Analysis," Information Systems Research, INFORMS, vol. 34(1), pages 67-84, March.
    20. Helmut Farbmacher & Martin Huber & Lukáš Lafférs & Henrika Langen & Martin Spindler, 2022. "Causal mediation analysis with double machine learning [Mediation analysis via potential outcomes models]," The Econometrics Journal, Royal Economic Society, vol. 25(2), pages 277-300.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:evarev:v:37:y:2013:i:5:p:405-431. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.