Causal Discovery: Foundations, Algorithms and Applications

The book is divided into three parts. The first part provides the necessary background on causal graphical models and causal reasoning. The second describes the main algorithms and techniques for causal discovery: (a) causal discovery from observational data, (b) causal discovery from interventional data, © causal discovery from temporal data, and (d) causal reinforcement learning. The third part provides several examples of causal discovery in practice, including applications in biomedicine, social sciences, artificial intelligence and robotics.