What has been largely overlooked, however, is the fact that these approaches are only valid in linear models and rely on the assumption that there is no interaction between exposure and mediator on the outcome (6). ...because the importance of controlling for mediator-outcome confounders was never mentioned in Baron & Kenny, many studies have neglected to account for potential confounding factors of the mediator-outcome relationship. ...whereas a total effect can always be decomposed into a natural direct and indirect effect, controlled direct effects are estimated for every level of the mediator (which may differ substantially depending on the magnitude of the interaction effect between exposure and mediator). An alternative approach in mediation analysis is to quantify controlled direct effects that only rely on assumptions (i) and (iii): no uncontrolled exposure-outcome confounding and no uncontrolled mediator-outcome confounding. ...some have argued that controlled direct effects are much more policy-relevant because they estimate the proportion of the total effect of the exposure on the outcome that could be eliminated by a specific intervention on the mediator (13, 14). ...sensitivity analysis is helpful to assess the robustness of the results to potential violations of the underlying assumptions.