Sensitivity Plots for Confounder Bias in the Single Mediator Model

Matthew G. Cox, Yasemin Kisbu-Sakarya, Milica Miočević, David Mackinnon

Research output: Contribution to journalArticlepeer-review

51 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)405-431
Number of pages27
JournalEvaluation Review
Issue number5
StatePublished - Oct 2014


  • causal inference
  • confounder bias
  • indirect effects
  • mediation
  • sensitivity analysis

ASJC Scopus subject areas

  • Arts and Humanities (miscellaneous)
  • General Social Sciences


Dive into the research topics of 'Sensitivity Plots for Confounder Bias in the Single Mediator Model'. Together they form a unique fingerprint.

Cite this