Name
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.5220/0008070503050309 | KDIR: PROCEEDINGS OF THE 11TH INTERNATIONAL JOINT CONFERENCE ON KNOWLEDGE DISCOVERY, KNOWLEDGE ENGINEERING AND KNOWLEDGE MANAGEMENT - VOL 1: KDIR |
Keywords | Field | DocType |
Causal Learning, Counterfactual Analysis, Cause and Effect, Supply Chain Vulnerability, Associations, Correlations, Lexical Link Analysis, Data Mining | Data science,Computer science,Link analysis,Artificial intelligence,Supply chain,Machine learning,Vulnerability | Conference |
Volume | Citations | PageRank |
2 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ying Zhao | 1 | 0 | 1.35 |
| Jacob Jones | 2 | 0 | 0.34 |
| Douglas J. MacKinnon | 3 | 0 | 1.01 |