- Bachelor Computer Science
- Bachelor Technical Communication
- Bachelor Education Computer Science
- Bachelor Erasmus
In this proseminar, we will discuss causality, causal inference, and causal reasoning, as described by Pearl. While there are many powerful statistical methods for analyzing data, these methods typically focus more on data description and less on finding causal relationships. Based on Pearl’s book on Causal Inference in Statistics, we will define a mathematical notion of causality and discuss several methods how these can be used for causal reasoning. This allows answering questions such as How effective is a given treatment in preventing a disease? and Had I taken the train, would I have arrived earlier?
Recommended prior knowledge
Basic knowledge of statistics
- J. Pearl, M. Glymour, and N. P. Jewell, Causal Inference in Statistics: A Primer. Wiley, 2016.