In this project, we investigate multiple aspects of causal modelling. We have investigated how causal models can be used to correct for selection bias and missing response when learning a regression model. In a follow-up project, we have investigated how the practical use of predictions can affect the value of the variable that is predicted, resulting in a so called ‘performative bias’. Using bias correction methods from the first project, we have provided solutions how to correct for performative bias. In current projects, we are looking into mathematical descriptions of causal models, with a focus on the ‘faithfulness property’ of nonparametric distributions, and causal modelling of functional data.