Chen et al. (Am J Epidemiol. 2013;177(9):870-881) develop a simulation study for comparing various measures of socioeconomic health disparities when bias can arise from temporal changes in the bivariate distribution of education and income. In this commentary, I argue that, in relation to health, the "meaning" of education cannot be reduced to its socioeconomic value; improved health literacy, for instance, can result in important health benefits. Further, I suggest that unless there is a substantial prior understanding of the data-generating mechanism, directed acyclic graph models should be avoided because causal relationships cannot be inferred from regression. An alternative is to resort to conditional independence graphs, which use only undirected edges. Finally, although the slope index of inequality can, in some specific cases, be seen to reduce bias in temporal comparisons of socioeconomic health disparities, it was not designed for causal inference. The slope index of inequality simply describes the average change in the proportion in poor health when the population is ordered by socioeconomic status.