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SUMMARY:Sparse Graphs and Causal Inference - Bhlmann\, P (ETH Zrich)
DTSTART:20100625T080000Z
DTEND:20100625T084500Z
UID:TALK25332@talks.cam.ac.uk
CONTACT:Mustapha Amrani
DESCRIPTION:Understanding cause-effect relationships between variables is 
 of interest in many fields of science.\nTo effectively address such questi
 ons\, we need to look beyond the framework of variable selection\nor impor
 tance from models describing associations only. We will show how graphical
  modeling and\nintervention calculus can be used for quantifying intervent
 ion and causal effects\, particularly for\nhigh-dimensional\, sparse setti
 ngs where the number of variables can greatly exceed sample size.\nBesides
  methodology and theory we illustrate some findings on gene intervention e
 ffects (of single\ngene deletions) in yeast.
LOCATION:Seminar Room 1\, Newton Institute
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