Whether teaching introductory students or PhD students, whether discussing randomized experiments or observational studies, the core fundamental idea I want to convey to students about causal inference remains the same: for causal inference, we want to compare groups that are comparable to begin with. In other words, we want all baseline variables (covariates) to be similarly distributed (balanced) between the groups being compared. In experiments, we achieve this through randomization and elements of experimental design. In observational studies, we can balance observed covariates by comparing matched units that are similar, looking within similar subclasses, or weighting units to have similar weighted covariate distributions; all of these can be illustrated with a single covariate or extended with propensity scores. While the depth and topic coverage will vary from course to course, the goal of balancing covariates, and helping students understand why this covariate balance is so crucial, remains the same.