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Conference item

Feature-to-feature regression for a two-step conditional independence test

Abstract:

The algorithms for causal discovery and more broadly for learning the structure of graphical models require well calibrated and consistent conditional independence (CI) tests. We revisit the CI tests which are based on two-step procedures and involve regression with subsequent (unconditional) independence test (RESIT) on regression residuals and investigate the assumptions under which these tests operate. In particular, we demonstrate that when going beyond simple functional relationships wit...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publication website:
http://auai.org/uai2017/accepted.php

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Institution:
University of Oxford
Oxford college:
Wadham College
Role:
Author
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Institution:
University of Oxford
Division:
MPLS
Department:
Statistics
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Statistics
Role:
Author
Publisher:
Association for Uncertainty in Artificial Intelligence Publisher's website
Host title:
Conference on Uncertainty in Artificial Intelligence
Publication date:
2017-08-15
Acceptance date:
2017-06-12
Event title:
Conference on Uncertainty in Artificial Intelligence (UAI 2017)
Event location:
Sydney, Australia
Event website:
http://auai.org/uai2017/index.php
Event start date:
2017-08-11T00:00:00Z
Event end date:
2017-08-15T00:00:00Z
ISSN:
1525-3384
Source identifiers:
700435
Language:
English
Pubs id:
pubs:700435
UUID:
uuid:bc3b78e3-ebe4-4f8d-8de1-8bcd11d660f8
Local pid:
pubs:700435
Deposit date:
2017-08-03

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