AUTHOR=Kisbu-Sakarya Yasemin , MacKinnon David P. , Valente Matthew J. , Çetinkaya Esra TITLE=Causal Mediation Analysis in the Presence of Post-treatment Confounding Variables: A Monte Carlo Simulation Study JOURNAL=Frontiers in Psychology VOLUME=11 YEAR=2020 URL=https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2020.02067 DOI=10.3389/fpsyg.2020.02067 ISSN=1664-1078 ABSTRACT=

In many disciplines, mediating processes are usually investigated with randomized experiments and linear regression to determine if the treatment affects the outcome through a mediator. However, randomizing the treatment will not yield accurate causal direct and indirect estimates unless certain assumptions are satisfied since the mediator status is not randomized. This study describes methods to estimate causal direct and indirect effects and reports the results of a large Monte Carlo simulation study on the performance of the ordinary regression and modern causal mediation analysis methods, including a previously untested doubly robust sequential g-estimation method, when there are confounders of the mediator-to-outcome relation. Results show that failing to measure and incorporate potential post-treatment confounders in a mediation model leads to biased estimates, regardless of the analysis method used. Results emphasize the importance of measuring potential confounding variables and conducting sensitivity analysis.