IDEAS home Printed from https://ideas.repec.org/a/sae/jedbes/v46y2021i1p85-108.html
   My bibliography  Save this article

Sensitivity Analysis for Pretreatment Confounding With Multiple Mediators

Author

Listed:
  • Soojin Park
  • Kevin M. Esterling

Abstract

The causal mediation literature has developed techniques to assess the sensitivity of an inference to pretreatment confounding, but these techniques are limited to the case of a single mediator. In this article, we extend sensitivity analysis to possible violations of pretreatment confounding in the case of multiple mediators. In particular, we develop sensitivity analyses under three alternative approaches to effect decomposition: (1) jointly considered mediators, (2) identifiable direct and indirect paths, and (3) interventional analogues effects. With reasonable assumptions, each approach reduces to a single procedure to assess sensitivity in the presence of simultaneous pre- and posttreatment confounding. We demonstrate our sensitivity analysis techniques with a framing experiment that examines whether anxiety mediates respondents’ attitudes toward immigration in response to an information prompt.

Suggested Citation

  • Soojin Park & Kevin M. Esterling, 2021. "Sensitivity Analysis for Pretreatment Confounding With Multiple Mediators," Journal of Educational and Behavioral Statistics, , vol. 46(1), pages 85-108, February.
    • Handle: RePEc:sae:jedbes:v:46:y:2021:i:1:p:85-108
    DOI: 10.3102/1076998620934500
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.3102/1076998620934500
    Download Restriction: no
    File URL: https://libkey.io/10.3102/1076998620934500?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. Rhian M. Daniel & Bianca L. De Stavola & Simon N. Cousens, 2011. "gformula: Estimating causal effects in the presence of time-varying confounding or mediation using the g-computation formula," Stata Journal, StataCorp LP, vol. 11(4), pages 479-517, December.
    2. Qingzhao Yu & Kaelen L. Medeiros & Xiaocheng Wu & Roxanne E. Jensen, 2018. "Nonlinear Predictive Models for Multiple Mediation Analysis: With an Application to Explore Ethnic Disparities in Anxiety and Depression Among Cancer Survivors," Psychometrika, Springer;The Psychometric Society, vol. 83(4), pages 991-1006, December.
    3. Ted Brader & Nicholas A. Valentino & Elizabeth Suhay, 2008. "What Triggers Public Opposition to Immigration? Anxiety, Group Cues, and Immigration Threat," American Journal of Political Science, John Wiley & Sons, vol. 52(4), pages 959-978, October.
    4. Sara Geneletti, 2007. "Identifying direct and indirect effects in a non‐counterfactual framework," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(2), pages 199-215, April.
    5. Carlos Cinelli & Chad Hazlett, 2020. "Making sense of sensitivity: extending omitted variable bias," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 82(1), pages 39-67, February.
    6. Stijn Vansteelandt & Tyler J. VanderWeele, 2012. "Natural Direct and Indirect Effects on the Exposed: Effect Decomposition under Weaker Assumptions," Biometrics, The International Biometric Society, vol. 68(4), pages 1019-1027, December.
    7. 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.
    8. Jeffrey M. Albert & Suchitra Nelson, 2011. "Generalized Causal Mediation Analysis," Biometrics, The International Biometric Society, vol. 67(3), pages 1028-1038, September.
    9. 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)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Park Soojin & Kang Suyeon & Lee Chioun & Ma Shujie, 2023. "Sensitivity analysis for causal decomposition analysis: Assessing robustness toward omitted variable bias," Journal of Causal Inference, De Gruyter, vol. 11(1), pages 1-23, January.

    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. 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.
    2. R. M. Daniel & B. L. De Stavola & S. N. Cousens & S. Vansteelandt, 2015. "Causal mediation analysis with multiple mediators," Biometrics, The International Biometric Society, vol. 71(1), pages 1-14, March.
    3. Tyler J. VanderWeele & Eric J. Tchetgen Tchetgen, 2017. "Mediation analysis with time varying exposures and mediators," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(3), pages 917-938, June.
    4. Viviana Celli, 2022. "Causal mediation analysis in economics: Objectives, assumptions, models," Journal of Economic Surveys, Wiley Blackwell, vol. 36(1), pages 214-234, February.
    5. Markus Frölich & Martin Huber, 2017. "Direct and indirect treatment effects–causal chains and mediation analysis with instrumental variables," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(5), pages 1645-1666, November.
    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. 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.
    9. 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.
    10. Martin Huber, 2015. "Causal Pitfalls in the Decomposition of Wage Gaps," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(2), pages 179-191, April.
    11. Hsu Yu-Chin & Huber Martin & Lai Tsung-Chih, 2019. "Nonparametric estimation of natural direct and indirect effects based on inverse probability weighting," Journal of Econometric Methods, De Gruyter, vol. 8(1), pages 1-20, January.
    12. Steen, Johan & Loeys, Tom & Moerkerke, Beatrijs & Vansteelandt, Stijn, 2017. "medflex: An R Package for Flexible Mediation Analysis using Natural Effect Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 76(i11).
    13. 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.
    14. Parker Hevron, 2018. "Judicialization and Its Effects: Experiments as a Way Forward," Laws, MDPI, vol. 7(2), pages 1-21, May.
    15. 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.
    16. 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.
    17. Giovanni Mellace & Alessandra Pasquini, 2019. "Identify More, Observe Less: Mediation Analysis Synthetic Control," CEIS Research Paper 474, Tor Vergata University, CEIS, revised 20 Nov 2019.
    18. Marco Doretti & Martina Raggi & Elena Stanghellini, 2022. "Exact parametric causal mediation analysis for a binary outcome with a binary mediator," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(1), pages 87-108, March.
    19. Park Soojin & Kang Suyeon & Lee Chioun & Ma Shujie, 2023. "Sensitivity analysis for causal decomposition analysis: Assessing robustness toward omitted variable bias," Journal of Causal Inference, De Gruyter, vol. 11(1), pages 1-23, January.
    20. Mellace, Giovanni & Pasquini, Alessandra, 2019. "Identify More, Observe Less: Mediation Analysis: Mediation Analysis Synthetic Control," Discussion Papers on Economics 12/2019, University of Southern Denmark, Department of Economics.

    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:jedbes:v:46:y:2021:i:1:p:85-108. 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.