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Author: Gianluca Bontempi

Affiliation: Université Libre de Bruxelles, Belgium

Keyword(s): Graphical Models, Causal Inference, Feature Selection.

Related Ontology Subjects/Areas/Topics: Feature Selection and Extraction ; Graphical and Graph-Based Models ; Pattern Recognition ; Theory and Methods

Abstract: Inferring causal relationships from observational data is still an open challenge in machine learning. State-of-the-art approaches often rely on constraint-based algorithms which detect v-structures in triplets of nodes in order to orient arcs. These algorithms are destined to fail when confronted with completely connected triplets. This paper proposes a criterion to deal with arc orientation also in presence of completely linearly connected triplets. This criterion is then used in a Relevance-Causal (RC) algorithm, which combines the original causal criterion with a relevance measure, to infer causal dependencies from observational data. A set of simulated experiments on the inference of the causal structure of linear networks shows the effectiveness of the proposed approach.

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Paper citation in several formats:
Bontempi, G. (2013). A Statistic Criterion for Reducing Indeterminacy in Linear Causal Modeling. In Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods - ICPRAM; ISBN 978-989-8565-41-9; ISSN 2184-4313, SciTePress, pages 159-166. DOI: 10.5220/0004254301590166

@conference{icpram13,
author={Gianluca Bontempi.},
title={A Statistic Criterion for Reducing Indeterminacy in Linear Causal Modeling},
booktitle={Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods - ICPRAM},
year={2013},
pages={159-166},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004254301590166},
isbn={978-989-8565-41-9},
issn={2184-4313},
}

TY - CONF

JO - Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods - ICPRAM
TI - A Statistic Criterion for Reducing Indeterminacy in Linear Causal Modeling
SN - 978-989-8565-41-9
IS - 2184-4313
AU - Bontempi, G.
PY - 2013
SP - 159
EP - 166
DO - 10.5220/0004254301590166
PB - SciTePress

- Science and Technology Publications, Lda.
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