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Sensitivity Analysis for Pretreatment Confounding With Multiple Mediators

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  • 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
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    References listed on IDEAS

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    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.
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    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.
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    Cited by:

    1. Park Soojin & Kang Suyeon & Ma Shujie & Lee Chioun, 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.

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