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CATEGORIES:Statistics
SUMMARY:What's the right complexity measure for inferring
causal relations? - Dominik Janzing (Max Planck In
stitute Tuebingen)
DTSTART;TZID=Europe/London:20100226T153000
DTEND;TZID=Europe/London:20100226T163000
UID:TALK22581AThttp://talks.cam.ac.uk
URL:http://talks.cam.ac.uk/talk/index/22581
DESCRIPTION:If X causes Y for two random variables X and Y\, w
e expect that the\nfactorization of P(X\,Y) into P
(X)P(Y|X) is simpler than the *non-causal*\nfactor
ization P(Y)P(X|Y). This is because P(Y|X) describ
es the causal\nmechanism while P(X|Y) is "only a m
athematical expression".\n\nDiscussions have shown
that a lot of researchers agree on this\nintuitio
n. Since we would like to use this principle for i
nferring\ncausal directions\, we are left with two
problems:\n\n(1) what does "simple" mean?\n\n(2)
is there any deeper justification for this princip
le?\n\nOur answer to question (2) is a clear "yes"
if complexity vs simplicity\nis measured in terms
of Kolmogorov complexity: I will present a theory
\nof causal inference that generalizes the framewo
rk of Bayesian networks\nto *algorithmic* instead
of *statistical* conditional dependences. I\nwill
show that our theory implies the above inference p
rinciple.\nHowever\, since Kolmogorov complexity i
s uncomputable we still need\ncomplexity measures
that are appropriate for practical implementations
.\n\nI will present some first small steps towards
this challenging goal.\n\n\n\n\nhttp://www.kyb.mp
g.de/~janzing
LOCATION:MR12\, CMS\, Wilberforce Road\, Cambridge\, CB3 0W
B
CONTACT:
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