Description: The course will approach concepts and methods for mediation from the perspective of the counterfactual framework. Mediation analysis quantifies the extent to which the effect of an exposure on some outcome is mediated through a particular intermediate and the extent to which it is direct or through other pathways. Definitions, identification results and statistical techniques related to mediation analysis will be covered. The course will clarify the assumptions required for the estimation of direct and indirect effect and will extend the approach to mediation typically employed in epidemiology and the social sciences to settings with interactions, non-linearities, and time-varying exposures. Prerequisite: Familiarity with regression analysis and potential outcomes.
Course Goals: Upon successful completion of this course, you should be able to:
• Explain when traditional methods for mediation fail
• Define the concepts about mediation from causal inference
• Conduct regression methods for mediation and interpret results of such analyses.
Learning Objectives: 1. To understand the assumptions of a counterfactual frame in
formulating mediation analyses questions
2. To identify different types of causal effects (e.g. total, direct,
indirect) and their mathematical relations with each other
3. To correctly specify regression models in conducting mediation analyses
2. To master the use of statistical software code to conduct mediation
analyses and the interpretation of output