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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
Version:
Accepted manuscript

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Department:
Oxford, Colleges and Halls, Wadham College
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Author
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Department:
Oxford, MPLS, Statistics
Role:
Author
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Department:
Oxford, MPLS, Statistics
Role:
Author
Publisher:
Association for Uncertainty in Artificial Intelligence Publisher's website
Publication date:
2017-08-15
Acceptance date:
2017-06-12
ISSN:
1525-3384
Pubs id:
pubs:700435
URN:
uri:bc3b78e3-ebe4-4f8d-8de1-8bcd11d660f8
UUID:
uuid:bc3b78e3-ebe4-4f8d-8de1-8bcd11d660f8
Local pid:
pubs:700435

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