Recommendations for improving causal inference of mediation analysis

Abstract

Mediation analysis is an indispensable tool for investigating how a treatment causally affects an outcome via intermediate variables. Recently, there have been increased concerns about the validity of causal inferences in conclusions drawn using mediation analysis. However, the discussions are limited to a single mediator, and importantly, there is a lack of guidelines on substantiating causal inferences. In this article, we first provide a thorough examination of the causal assumptions underpinning mediation analysis. We pay particular attention to the practice of exploring mediated effects along various paths linking several mediators and the stringent -- yet often overlooked -- assumptions that predicate valid inference. To mitigate the risk of invalid inference, we introduce an alternative approach focusing on mediator-specific indirect effects. An appealing feature of this approach is that valid causal inference of mediation analysis with multiple mediators does not necessitate assuming a (correct) causal structure among the mediators. Finally, we provide a practical guide to improve the research practice of mediation analysis. We clarify when mediation analysis is (in)appropriate; when appropriate, we recommend that researchers preregister (i) justifications of the asserted causal relations in their mediation analysis, (ii) pre-treatment confounders, and (iii) the either path- or mediator-specific indirect effects to be investigated. Confounding adjustment when estimating the preregistered indirect effects and sensitivity analyses for unmeasured confounding can fortify causal inferences in the conclusions. We hope this article will encourage explication, justification, and reflection of the causal assumptions underpinning mediation analysis to improve the validity of causal inferences in psychology research.