For over two decades, disease management (DM) has been touted as an intervention capable of producing large scale cost savings for health care purchasers. However, the preponderance of scientific evidence suggests that these programs do not save money. This finding is not surprising given that the theorized causal mechanism by which the intervention supposedly influences the outcome has not been systematically assessed. Mediation analysis is a statistical approach to identifying causal pathways by testing the relationships between the treatment, the outcome, and an intermediate variable that is posited to mediate the relationship between the treatment and outcome. This analysis can therefore help identify how to make DM interventions effective by determining the causal mechanisms between intervention components and the desired outcome. DM interventions can then be optimized by eliminating those activities that are ineffective or even counter-productive. In this article we seek to promote the application of mediation analysis to DM program evaluation by describing the two principal frameworks generally followed in causal mediation analysis; structural equation modeling and potential outcomes. After comparing several approaches within these frameworks using real and simulated data, we find that some methods perform better than others under the conditions imposed upon the models. We conclude that mediation analysis can assist DM programs in developing and testing the causal pathways that enable interventions to be effective in achieving desired outcomes.