Demonstration of Conceptual and Statistical Assumptions of Causal Mediation

Mediation analysis has a long history, with numerous examples of mediation being tested in clinical trials and observational studies. Mediation analysis focuses on outcome mechanisms, providing a lens for understanding the why behind clinical outcomes. While mediation is a statistical method, it is as much, if not more, about study design, assumptions, and conceptual underpinnings. Several authors have already provided excellent summaries of the state of the science of mediation in nursing research and statistical tutorials; however, barriers remain to adopting robust mediation methods.

Provide an accessible introduction to causal mediation, encourage research designs that incorporate principles of causal inference, and provide a straightforward explanation of the assumptions inherent in study design and statistical modeling in causal mediation.

We first review causal mediation assumptions, including practical examples relevant to nursing. Then we walk through an example demonstration of assumptions in the context of one of our own studies. We conclude with a pragmatic discussion of common considerations when incorporating mediation into a study design.

Through the illustrative example, we demonstrate how to evaluate core assumptions of causal mediation in practice and how design decisions influence the plausibility of meeting them. The example serves as a practical template for applying causal mediation concepts in nursing research.

The quality of causal inference in mediation analysis depends on research design as much as it does on statistical sophistication. Mediation analysis is most powerful when researchers treat its causal assumptions as design principles rather than technical add-ons. No amount of post-hoc statistical adjustment can fully compensate for weaknesses in measurement, temporality, or study conceptualization.