Abstract Details
Activity Number:
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110
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Type:
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Topic Contributed
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Date/Time:
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Monday, August 4, 2014 : 8:30 AM to 10:20 AM
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Sponsor:
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ENAR
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Abstract #313750
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Title:
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Nested Markov Models for Structure Learning in the Presence of Confounding
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Author(s):
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Thomas Richardson*+ and James Robins and Ilya Shpitser and Robin J. Evans
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Companies:
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University of Washington and Harvard School of Public Health and University of Southampton and University of Oxford
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Keywords:
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Causal Inference ;
Hidden Variables ;
Graphical Models
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Abstract:
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Causal DAG models provide a flexible class of multivariate causal models. It has been known for some time that DAG models with unobserved variables imply non-parametric constraints over the observed margin. In this talk we will describe the structure of these constraints via a nested Markov property.
(Joint work with Robin Evans, James Robins and Ilya Shpitser.)
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Authors who are presenting talks have a * after their name.
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