Causal reasoning is important in many areas, including the sciences, decision making and public policy. The gold standard method for determining causal relationships uses randomized controlled perturbation experiments. In many settings, however, such experiments are expensive, time consuming or impossible. Hence, it is worthwhile to obtain causal information from observational data, that is, from data obtained by observing the system of interest without subjecting it to interventions. In this talk, I will discuss approaches for causal learning from observational data, paying particular attention to the combination of causal structure learning and variable selection, with the aim of estimating causal effects. Throughout, examples will be used to illustrate the concepts.