The author begins this discussion by thanking Larry Hedges, the editor of the journal, for giving him an opportunity to provide a commentary on this stimulating article. He also would like to congratulate the authors of the article for their insightful discussion on causal mediation analysis, which is one of the most important and challenging methodological problems we face in the literature of causal inference. He particularly admires the authors' efforts to clearly explain complex statistical concepts in the context of their specific application. He believes that applied education researchers who read this article will gain a better understanding of important methodological issues regarding causal mediation analysis. Although there are many positive things to be said about the authors' proposed methodology, in this commentary the author discusses one potential way to further improve it. In particular, the authors argue that the proposed ratio-of-mediator-probability weighted (RMPW) estimation methodology is attractive because unlike some of the existing methods it "does not involve explicit modeling of the mediator-outcome relationship". Although this is clearly an advantage, the RMPW estimation methodology still requires researchers to correctly model the mediator given the treatment and pretreatment confounders. This is often a challenging task because there may exist a large number of pretreatment confounders. Because nonparametric modeling in a high-dimensional covariate space is difficult, these confounders often must be adjusted through a parametric model.