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SUMMARY:Identification is easy\, factorization is hard - Ilya Shpitser (Jo
 hns Hopkins University)
DTSTART:20260123T110000Z
DTEND:20260123T114500Z
UID:TALK241843@talks.cam.ac.uk
DESCRIPTION:It is well known that in fully observed causal directed acycli
 c graph (DAG) model\, all causal effects are identified via a truncated fa
 ctorization of a DAG known as the g-formula.&nbsp\; In the presence of hid
 den variables\, the situation is significantly more complicated.&nbsp\; Ma
 ny causal effects are no longer identified\, and identification of causal 
 effects is attained by means of a recursive method known as the ID algorit
 hm.In this talk\, I demonstrate how the ID algorithm\, and its extensions 
 for mediation analysis\, selection bias\, data fusion\, may be viewed as a
  truncated factorization of a model of a mixed graph.&nbsp\; This view not
  only simplified the view of nonparametric identification theory in graphi
 cal models\, but provides a direct connection between functionals output b
 y the ID algorithm to plug-in and semi-parametric estimators.
LOCATION:Seminar Room 1\, Newton Institute
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