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Main Title Causal analytics for applied risk analysis /
Author Cox Jr., Louis Anthony,
Other Authors
Author Title of a Work
Popken, Douglas A.,
Sun, Richard X.,
Publisher Springer,
Year Published 2018
OCLC Number 1024263404
ISBN 3319782401; 9783319782409
Subjects Business ; Big data ; Risk management
Holdings
Library Call Number Additional Info Location Last
Modified
Checkout
Status
ELBM  HD30.23.C69 2018 AWBERC Library/Cincinnati,OH 08/20/2019
Collation xxii, 588 pages : illustrations ; 24 cm.
Notes
Includes bibliographical references and index.
Contents Notes
Causal analytics methods can revolutionize the use of data to make effective decisions by revealing how different choices affect probabilities of various outcomes. This book presents and illustrates models, algorithms, principles, and software for deriving causal models from data and for using them to optimize decisions with uncertain outcomes. It discusses how to describe and summarize situations; detect changes; evaluate effects of policies or interventions; learn what works best under different conditions; predict values of as-yet unobserved quantities from available data; and identify the most likely explanations for observed outcomes, including surprises and anomalies. The book resents practical techniques for causal modeling and analytics that practitioners can apply to improve understanding of how choices affect probabilities of consequences and, based on this understanding, to recommend choices that are more likely to accomplish their intended objectives.