Casual Inference

Teacher: Alexander Gebharter

Content: This course builds on basic insights established in the course “Causation and Probabilities” and some of the formal tools introduced in the course “Formal Epistemology”. It further advances topics from these courses and provides an introduction to causal models and causally interpreted Bayesian networks. These tools can be used to formulate complex causal hypotheses more precisely, to generate probabilistic predictions on the basis of observation and hypothetical intervention, and to uncover causal structure from observational and experimental data. The course combines content and will allow students to familiarize themselves with these tools by applying them to different tasks and toy examples.