In difference-in-differences studies, the so-called parallel trends assumption carries a heavy burden. The pursuit of parallel trends can sometimes mask an important discussion on model choice, assumptions, and confounders and their effects on our diff-in-diff estimates. In this talk, we focus on the definition of a confounder in diff-in-diff and develop strategies to adjust for it. Further, we discuss how to leverage covariates to achieve efficiency gains (smaller standard errors) in our diff-in-diff estimates. We conclude by discussing causal quantities and identifying assumptions for diff-in-diff when you have many post-treatment time points.