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Authors: Ying Zhao ; Jacob Jones and Douglas MacKinnon

Affiliation: Naval Postgraduate School, Monterey, CA and U.S.A.

ISBN: 978-989-758-382-7

Keyword(s): Causal Learning, Counterfactual Analysis, Cause and Effect, Supply Chain Vulnerability, Associations, Correlations, Lexical Link Analysis, Data Mining.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Business Analytics ; Computational Intelligence ; Data Analytics ; Data Engineering ; Evolutionary Computing ; Information Extraction ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Machine Learning ; Soft Computing ; Structured Data Analysis and Statistical Methods ; Symbolic Systems

Abstract: This paper illustrates a methodology of causal learning using pair-wise associations discovered from data. Taking advantage of a U.S. Department of Defense supply chain use case, this causal learning approach was substantiated and demonstrated in the application of discovering supply chain vulnerabilities. By integrating lexical link analysis, a data mining tool used to discover relationships in specific vocabularies or lexical terms with pair-wise causal learning, supply chain vulnerabilities were recognized. Evaluation of results from this methodology reveals supply chain opportunities, while exposing weaknesses to develop a more responsive and efficient supply chain system.

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Paper citation in several formats:
Zhao, Y.; Jones, J. and MacKinnon, D. (2019). Causal Learning to Discover Supply Chain Vulnerability.In Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, ISBN 978-989-758-382-7, pages 305-309. DOI: 10.5220/0008070503050309

@conference{kdir19,
author={Ying Zhao. and Jacob Jones. and Douglas MacKinnon.},
title={Causal Learning to Discover Supply Chain Vulnerability},
booktitle={Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR,},
year={2019},
pages={305-309},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008070503050309},
isbn={978-989-758-382-7},
}

TY - CONF

JO - Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR,
TI - Causal Learning to Discover Supply Chain Vulnerability
SN - 978-989-758-382-7
AU - Zhao, Y.
AU - Jones, J.
AU - MacKinnon, D.
PY - 2019
SP - 305
EP - 309
DO - 10.5220/0008070503050309

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