Lecture on July 5 will be again online...

Written on 04.07.22 by Markus Bläser

... for the same reason as last time. We use the same link as last time (materials section).

Lecture on June 28 will be online!

Written on 27.06.22 (last change on 28.06.22) by Markus Bläser

You can find the zoom link in the materials section

No lecture today

Written on 26.04.22 by Markus Bläser

Recall 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.



  • Lecture Tue, 14 - 16, E1.3 HS001
  • There will be assignments. Solutions will be discussed after the lecture. The room will be announced.



There will be oral exams at the end of the semester.



  • 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.
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