• Thomas R Ten Have and Marshall M Joffe.
• Department of Biostatistics and Epidemiology, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA. ttenhave@upenn.edu
• Stat Methods Med Res. 2012 Feb 1;21(1):77-107.

AbstractWe describe causal mediation methods for analysing the mechanistic factors through which interventions act on outcomes. A number of different mediation approaches have been presented in the biomedical, social science and statistical literature with an emphasis on different aspects of mediation. We review the different sets of assumptions that allow identification and estimation of effects in the simple case of a single intervention, a temporally subsequent mediator and outcome. These assumptions include various no confounding assumptions including sequential ignorability assumptions and also interaction assumptions involving the treatment and mediator. The understanding of such assumptions is crucial since some can be assessed under certain conditions (e.g. treatment-mediator interactions), whereas others cannot (sequential ignorability). These issues become more complex with multiple mediators and longitudinal outcomes. In addressing these assumptions, we review several causal approaches to mediation analyses.

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