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GRAPHICAL METHODS FOR INEQUALITY CONSTRAINTS IN MARGINALIZED DAGS

Abstract:

We present a graphical approach to deriving inequality constraints for directed acyclic graph (DAG) models, where some variables are unobserved. In particular we show that the observed distribution of a discrete model is always restricted if any two observed variables are neither adjacent in the graph, nor share a latent parent; this generalizes the well known instrumental inequality. The method also provides inequalities on interventional distributions, which can be used to bound causal effe...

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Publication status:
Published

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Publisher copy:
10.1109/MLSP.2012.6349796

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Institution:
University of Oxford
Division:
MPLS
Department:
Statistics
Role:
Author
Journal:
2012 IEEE INTERNATIONAL WORKSHOP ON MACHINE LEARNING FOR SIGNAL PROCESSING (MLSP)
Publication date:
2012-01-01
DOI:
EISSN:
2161-0371
ISSN:
2161-0363
Source identifiers:
425332
Language:
English
Keywords:
Pubs id:
pubs:425332
UUID:
uuid:03e04980-63d5-4d3e-af8d-3764747f7af5
Local pid:
pubs:425332
Deposit date:
2013-11-16

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