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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 causal effect of an intervention (treatment/exposure) on an outcome can be estimated by: i) specifying knowledge about the data-generating process; ii) assessing under what assumptions a target quantity, such as for example a causal odds ratio, can be identified given the specified knowledge (and given the measured data); and then, iii) using appropriate statistical estimation techniques to estimate the desired parameter of interest.

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.