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CATEGORIES:Causal Inference Seminar and Discussion Group
SUMMARY:Dormant independence - Dr Ricardo Silva\, Dept of
Statistics\, UCL
DTSTART;TZID=Europe/London:20090302T163000
DTEND;TZID=Europe/London:20090302T173000
UID:TALK16651AThttp://talks.cam.ac.uk
URL:http://talks.cam.ac.uk/talk/index/16651
DESCRIPTION:The construction of causal graphs from non-experim
ental data rests on a set of constraints that the
graph structure imposes on all probability distrib
utions compatible with the graph. These constraint
s are of two types: conditional independencies and
algebraic constraints\, first noted by Verma. Whi
le conditional independencies are well studied and
frequently used in causal induction algorithms\,
Verma constraints are still poorly understood\, an
d rarely applied.\nThis paper examines a special s
ubset of Verma constraints which are easy to under
stand\, easy to identify and easy to apply\; they
arise from dormant independencies\, namely\, condi
tional independencies that hold in interventional
distributions.\nA complete algorithm is given for
determining if a dormant independence between two
sets of variables is entailed by the causal graph\
, such that this independence is identifiable\, in
other words if it resides in an interventional di
stribution that can be predicted without resorting
to interventions. The usefulness of dormant indep
endencies is shown in model testing and induction
by giving an algorithm that uses constraints entai
led by dormant independencies to prune extraneous
edges from a given causal graph.\n\n'Dormant indep
endence'\, I. Shpitset and J. Pearl\, UCLA Cogniti
ve Systems Laboratory\, Technical Report (R-340)\,
April 2008. In Proceedings of the Twenty-Third Co
nference on Artificial Intelligence\, 1081-1087\,
2008 http://bayes.cs.ucla.edu/csl_papers.html\n
LOCATION:Centre for Mathematical Sciences\, MR15
CONTACT:Dr Clive Bowsher
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