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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Veröffentlicht in:PROBLEMS IN PROGRAMMING
Datum:2018
Heft:2-3
Сторінки:180-188
ISSN:1727-4907
Автори та афіліації:
  • O.S. Balabanov — Institute of Software Systems NAS of Ukraine — ORCID: 0000-0001-9141-9074
Ключові слова:умовна незалежність 1-го рангу, умовна незалежність, локальномінімальний сепаратор, d-сепарація, безумовна незалежність, колізор, каузальна мережа, ланцюг, ланцюжок, каузальні моделі
1. Verfasser: Balabanov, O.S.
Format: Artikel
Sprache:Ukrainisch
Veröffentlicht: PROBLEMS IN PROGRAMMING 2018
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Online Zugang:https://pp.isofts.kiev.ua/index.php/ojs1/article/view/281
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Назва журналу:Problems in programming
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Problems in programming
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Zusammenfassung: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
ISSN:1727-4907
DOI:10.15407/pp2018.02.180