Causality for Complexity Theorists

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 Thu, 10:15 - 12:00, E1.3 HS001
• Tutorials Wed: 12:15 - 14:00, room tba. First tutorial Apr 30

 

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.
• D. Koller, N. Friedman: Probabilistic Graphical Models: Principles and Techniques, MIT Press, 2009