Causality for Complexity Theorists Markus Bläser

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26.04.2022

No lecture today

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.

 

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.


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