Simulation is an important tool within epidemiology for both learning and developing new methodology (1–5). Unfortunately, few epidemiology training programs teach basic simulation methods. Briefly, when conducting a simulation experiment, we generally follow the same basic steps. We first decide which variables to include, as well as their distributions and associations—often aided by a causal diagram. We then generate those variables by sampling from their specified distributions and estimate whatever target parameter is of interest (e.g., sample average or causal effect). We finally repeat the process multiple times, building a distribution for the target parameter from the estimates obtained in each replicate. Here, we briefly demonstrate one key simulation practice: the balancing intercept, which allows us to specify in the first step above the marginal mean of a variable and see that mean preserved (in expectation) in the simulated sample. This is attractive because the marginal...