Adjusting for Baseline Measurements of the Mediators and Outcome as a First Step Toward Eliminating Confounding Biases in Mediation Analysis

Mediation analysis prevails for researchers probing the etiological mechanisms through which treatment affects an outcome. A central challenge of mediation analysis is justifying sufficient baseline covariates that meet the causal assumption of no unmeasured confounding. But current practices routinely overlook this assumption. In this article, we suggest a relatively easy way to mitigate the risks of incorrect inferences resulting from unmeasured confounding: include pretreatment measurements of the mediator(s) and the outcome as baseline covariates. We explain why adjusting for pretreatment baseline measurements is a necessary first step toward eliminating confounding biases. We hope that such a practice can encourage explication, justification, and reflection of the causal assumptions underpinning mediation analysis toward improving the validity of causal inferences in psychology research.