For many analyses, it is common to use both bootstrapping and multiple imputation (MI): MI to address missing data and bootstrapping to obtain standard errors. For example, when using the g-formula in causal inference, bootstrapping is required to obtain standard errors; however, the data may be multiply imputed due to missing (baseline) data in the population of interest.