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Activity Number: 110
Type: Topic Contributed
Date/Time: Monday, August 4, 2014 : 8:30 AM to 10:20 AM
Sponsor: ENAR
Abstract #313750
Title: Nested Markov Models for Structure Learning in the Presence of Confounding
Author(s): Thomas Richardson*+ and James Robins and Ilya Shpitser and Robin J. Evans
Companies: University of Washington and Harvard School of Public Health and University of Southampton and University of Oxford
Keywords: Causal Inference ; Hidden Variables ; Graphical Models
Abstract:

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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