Recently, there has been a lot of interest and discussion about the use of causal inference in economics. Whether it is feasible and whether there are benefits of working with a directed acyclic graph (DAG) and a non-parametric structural equation framework, is one aspect of the debate.

The Republic of South Africa (SA) faced repeated episodes of temporary power shutdowns in 2014/2015, but also in the years thereafter. Based on my co-author’s experience at the burns unit at Red Cross children’s hospital, we had the hypothesis that this may have caused an increase in pediatric hospital admissions.

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.