# A Statistic Criterion for Reducing Indeterminacy in Linear Causal Modeling

### Gianluca Bontempi

#### 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 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