A Statistic Criterion for Reducing Indeterminacy in Linear Causal Modeling

Gianluca Bontempi

2013

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.

Paper Citation

in Harvard Style

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 - Volume 1: ICPRAM, ISBN 978-989-8565-41-9, pages 159-166. DOI: 10.5220/0004254301590166

in Bibtex Style

@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 - Volume 1: ICPRAM,},
year={2013},
pages={159-166},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004254301590166},
isbn={978-989-8565-41-9},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - A Statistic Criterion for Reducing Indeterminacy in Linear Causal Modeling
SN - 978-989-8565-41-9
AU - Bontempi G.
PY - 2013
SP - 159
EP - 166
DO - 10.5220/0004254301590166