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University of Cambridge > Talks.cam > Statistics > What's the right complexity measure for inferring causal relations?

## What's the right complexity measure for inferring causal relations?Add to your list(s) Download to your calendar using vCal - Dominik Janzing (Max Planck Institute Tuebingen)
- Friday 26 February 2010, 15:30-16:30
- MR12, CMS, Wilberforce Road, Cambridge, CB3 0WB.
If you have a question about this talk, please contact rbg24. If X causes Y for two random variables X and Y, we expect that the
factorization of P(X,Y) into P(X)P(Y|X) is simpler than the Discussions have shown that a lot of researchers agree on this intuition. Since we would like to use this principle for inferring causal directions, we are left with two problems: (1) what does “simple” mean? (2) is there any deeper justification for this principle? Our answer to question (2) is a clear “yes” if complexity vs simplicity
is measured in terms of Kolmogorov complexity: I will present a theory
of causal inference that generalizes the framework of Bayesian networks
to I will present some first small steps towards this challenging goal. http://www.kyb.mpg.de/~janzing This talk is part of the Statistics series. ## This talk is included in these lists:- All CMS events
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