In policy evaluations, interest may focus on why a particular treatment works. One tool for understanding why treatments work is causal mediation analysis. In this essay, I focus on the assumptions needed to estimate mediation effects. I show that there is no "gold standard" method for the identification of causal mediation effects. In particular, mediation effects will always have the character of estimates from observational data since they are generally subject to a specific form of confounding. Additionally, I demonstrate how randomization of the mediator and instrumental variables methods lead to fundamentally different quantities than causal mediation analyses. I also review recent work that discusses how the assumptions of mediation analyses differ when there is treatment noncompliance or when there are multiple mediators. Throughout, I motivate concepts using path diagrams and an example of a classroom intervention designed to increase test scores.