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Sensitivity plots for confounder bias in the single mediator model
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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. In this paper, we compare and contrast three different methods for assessing sensitivity to confounding and describes the graphical depiction of these methods.
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. The non-simulated data was 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. We discuss how these methods could be implemented in future mediation studies as well as the limitations and future directions for these methods. Reference: Cox, M. G., Kisbu-Sakarya, Y., Miočević, M., & MacKinnon, D. P. (2013). Sensitivity Plots for Confounder Bias in the Single Mediator Model. Evaluation Review, 37, 1-27. Link |