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.
Keywords: causal inference; confounder bias; indirect effects; mediation; sensitivity analysis.