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On the classes of causal networks, identifiable by simple independence tests

On the classes of causal networks, identifiable by simple independence tests

We tackle some theoretical problems of constraint-based approach to causal network inference from data (without prior restrictions). Our interest is to recover a model structure from independence tests of zero and first rank only. Class of 1-identifiable causal structures is defined. An idea to reco...

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Bibliographic Details
Main Author: Balabanov, O.S.
Format: Article
Language:Ukrainian
Published: Інститут програмних систем НАН України 2018
Subjects:
causal network
1-identifiable model
unconditional independence
conditional independence of 1st rank
locally minimal separator
collider
cycle
chain
d-separation
poly-tree
edge triangle
UDC 004.855:519.216
Online Access:https://pp.isofts.kiev.ua/index.php/ojs1/article/view/281
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Summary:We tackle some theoretical problems of constraint-based approach to causal network inference from data (without prior restrictions). Our interest is to recover a model structure from independence tests of zero and first rank only. Class of 1-identifiable causal structures is defined. An idea to recognize whether model recovery is successfully completed (i.e. adequate model structure is outputted) is suggested. Theframework of locally minimal separation in DAG is shown to be appropriate instrument to tackle the problem. A few subclasses of class of 1-identifiable structures are specified; corresponding structural restrictions and criteria of recovery completeness are given. We present some causal structures which are not 1-identifiable.Problems in programming 2018; 2-3: 180-188

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