The goal of this study is to better understand how methods for estimating treatment effects of latent groups operate. In particular, we identify where violations of assumptions can lead to biased estimates, and explore how covariates can be critical in the estimation process. For each set of approaches, we first review the assumptions necessary for identification and discuss practical issues that arise in estimation. We then examine how covariates allow for improved estimation, and determine the conditions necessary for using covariates to identify causal effects in latent groups. We then compare the different methods using simulation studies built from datasets constructed by imputing missing class membership and potential outcomes from real-world studies. This allows for examining the performance of the different techniques under a variety of plausible circumstances. We finally apply these methods to two common data sets that represent the type of data increasingly available to researchers, the JOBS II study and the Head Start Impact Study (HSIS), and compare the resulting treatment effect estimates to each other and some plausible baseline values. (author abstract)