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SUMMARY:It is hard to be strongly faithful - Caroline Uhler\, Institute of
  Science and Technology Austria
DTSTART:20140502T150000Z
DTEND:20140502T160000Z
UID:TALK52179@talks.cam.ac.uk
CONTACT:20082
DESCRIPTION:Many algorithms for inferring causality are based on partial c
 orrelation testing. Partial correlations define hypersurfaces in the param
 eter space of a directed Gaussian graphical model. The volumes obtained by
  bounding partial correlations play an important role for the performance 
 of causal inference algorithms. By computing these volumes we show that th
 e so-called "strong-faithfulness assumption"\, one of the main constraints
  of many causal inference algorithms\, is in fact extremely restrictive\, 
 implying fundamental limitations for these algorithms. We then propose an 
 alternative method that involves finding the permutation of the variables 
 that yields the sparsest DAG. In the Gaussian setting\, our sparsest permu
 tation (SP) algorithm boils down to determining the permutation with spars
 est Cholesky decomposition of the inverse covariance matrix. We prove that
  the constraints required for our SP algorithm are strictly weaker than st
 rong-faithfulness and are necessary for any causal inference algorithm bas
 ed on conditional independence testing.
LOCATION:MR12\,  Centre for Mathematical Sciences\, Wilberforce Road\, Cam
 bridge
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