Authors:
- Highlights causal analytics methods that answer how changing decision variables can change probabilities of various outcomes
- Presents models, algorithms, principles, and software for deriving causal models from data in order to optimize decisions
- Surveys modern analytics methods useful for decision, risk, and policy analysis
Part of the book series: International Series in Operations Research & Management Science (ISOR, volume 270)
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Table of contents (15 chapters)
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Front Matter
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Concepts and Methods of Causal Analytics
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Front Matter
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Descriptive Analytics in Public and Occupational Health
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Front Matter
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Predictive and Causal Analytics
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Front Matter
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Risk Management: Insights from Prescriptive, Learning, and Collaborative Analytics
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Front Matter
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About this book
Authors and Affiliations
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Cox Associates, Denver, USA
Louis Anthony Cox Jr.
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Cox Associates, Littleton, USA
Douglas A. Popken
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Cox Associates, East Brunswick, USA
Richard X. Sun
About the authors
Bibliographic Information
Book Title: Causal Analytics for Applied Risk Analysis
Authors: Louis Anthony Cox Jr., Douglas A. Popken, Richard X. Sun
Series Title: International Series in Operations Research & Management Science
DOI: https://doi.org/10.1007/978-3-319-78242-3
Publisher: Springer Cham
eBook Packages: Business and Management, Business and Management (R0)
Copyright Information: Springer International Publishing AG, part of Springer Nature 2018
Hardcover ISBN: 978-3-319-78240-9Published: 05 July 2018
Softcover ISBN: 978-3-030-08653-4Published: 24 January 2019
eBook ISBN: 978-3-319-78242-3Published: 19 June 2018
Series ISSN: 0884-8289
Series E-ISSN: 2214-7934
Edition Number: 1
Number of Pages: XXII, 588
Number of Illustrations: 26 b/w illustrations, 95 illustrations in colour
Topics: Operations Research/Decision Theory, Big Data/Analytics, Risk Management