After explaining the necessity of causal models and discussing some principles of causal inference, the book teaches readers how to apply causal models: how to calculate intervention distributions, how to derive causal models from observational and intervention data, and how causal concepts can be utilized for classic problems in machine learning. All these topics are initially addressed in the context of two variables and then in the more general multivariate case. The bivariate case proves to be particularly challenging for causal learning, as there are no conditional independencies, which are typically used by classical methods to solve multivariate cases. The authors consider the analysis of statistical asymmetries between cause and effect to be extremely enlightening and report on their decade of intensive research on this issue.