  Despite the major advances taken in causal modeling, causality is still an
unfamiliar topic for many statisticians. In this paper, it is demonstrated from
the beginning to the end how causal effects can be estimated from observational
data assuming that the causal structure is known. To make the problem more
challenging, the causal effects are highly nonlinear and the data are missing
at random. The tools used in the estimation include causal models with design,
causal calculus, multiple imputation and generalized additive models. The main
message is that a trained statistician can estimate causal effects by
judiciously combining existing tools.
