Graphical Models & Causal Reasoning

Graphical models provide a general framework for describing statistical relations between random variables and performing inference. More recently, they have become a popular framework for reasoning about causal relations. Graphical models are employed in a wide range of applications, from modelling gene regulatory networks to studying interactions in social groups. In this seminar, we will introduce the basics of inference in graphical models, discuss how they can be employed to perform causal inference, study causal reasoning in directed acyclic graphs (DAGs), and critically examine recent theoretical advances and real-world applications.