Cluster robust models are a kind of statistical models that attempt to estimate parameters considering potential heterogeneity in treatment effects. Absent heterogeneity in treatment effects, the partial and average treatment effect are the same. When heterogeneity in treatment effects occurs, the average treatment effect is a function of the various partial treatment effects and the composition of the population of interest. The first chapter explores the performance of common estimators as a function of the presence of heterogeneity in treatment effects and other characteristics that may influence their performance for estimating average treatment effects. The second chapter examines various approaches to evaluating and improving cluster structures as a way to obtain cluster-robust models. Both chapters are intended to be useful to practitioners as a how-to guide to examine and think about their applications and relevant factors. Empirical examples are provided to illustrate theoretical results, showcase potential tools, and communicate a suggested thought process.