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Sensitivity Plots for Confounder Bias in the Single Mediator Model

Author

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  • 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
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    References listed on IDEAS

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    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.
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