News
04.07.2022

Lecture on July 5 will be again online...... for the same reason as last time. We use the same link as last time (materials section). 
27.06.2022

Lecture on June 28 will be online!You can find the zoom link in the materials section 
26.04.2022

No lecture todayRecall that there is no lecture today (Apr 26). The next lecture is May 3. Markus Bläser 
Causality for Complexity Theorists
Probabilistic causation is a philiosophical and mathematical concept that aims to characterize the relationship between cause and effect using the tools of probability theory. Given some probabilities (some people call it "data"), we try to infer what is the cause and what is the effect.
The theory and mathematics behind causality have a lot of connections to theoretical computer science and complexity. We will explore these connections in this lecture.
You will not see any data in this lecture.
Time
 Lecture Tue, 14  16, E1.3 HS001
 There will be assignments. Solutions will be discussed after the lecture. The room will be announced.
Exams
There will be oral exams at the end of the semester.
Literature:
 J. Pearl, Causality: Models, Reasoning, and Inference, Cambridge University Press, 2000.
 J. Pearl, M. Glymour, N.P. Jewell, Causal Inference in Statistics: A Primer, Wiley, 2016.