Experimental evaluations are increasingly common in the U.S. educational policy-research context. Often, in investigations of multifaceted interventions, researchers and policymakers alike are interested in not only "whether" a given intervention impacted an outcome but also "why". What "features" of the intervention led to the impacts observed, or what was the "causal mechanism" or "pathway" through which treatment assignment resulted in an improved outcome? Quantitative methods for modeling such mediational processes appropriately are an active area of methodological exploration. I contribute to this literature by discussing an approach that relies on the analytic framework of principal stratification. Approaches, such as regression analysis and instrumental variables estimation (a special case of principal stratification), rely on assumptions--sequential ignorability and an exclusion restriction, respectively--that may be too strong to be plausible in practical settings. Principal stratification provides a broader framework with which these assumptions may be relaxed, although in exchange for other sets of assumptions. To illustrate and critique, I apply the principal stratification framework and Bayesian estimation techniques to data from MDRC's experimental evaluation of career academy high schools to explore the hypothesis that students' increased exposure to the world-of-work through the career academy treatment serves as a causal mechanism of subsequent improvements in earnings in the years following high school. Whereas many applications of the principal stratification framework consider a mediator measured on a binary scale, this article contributes an example in which a three-category measure of the mediator is considered. (Contains 3 tables, 5 figures and 15 footnotes.)