News

Currently, no news are available

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
  • There will be assignments. The slot for the tutorial will be discussed in the second lecture on Apr. 17

 

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.
Privacy Policy | Legal Notice
If you encounter technical problems, please contact the administrators.