Overview
- Comprehensive reference for those interested in the cause-effect problem, and how to tackle them using machine learning algorithms
- Includes six tutorial chapters, beginning with the simplest cases and common methods, to algorithmic methods that solve the cause-effect pair problem
- Supplemental material includes videos, slides, and code which can be found on the workshop website
Part of the book series: The Springer Series on Challenges in Machine Learning (SSCML)
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Table of contents (14 chapters)
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Fundamentals
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Selected Readings
Keywords
About this book
This book provides both tutorial material on the state-of-the-art on cause-effect pairs and exposes the reader to more advanced material, with a collection of selected papers. Supplemental material includes videos, slides, and code which can be found on the workshop website.
Discovering causal relationships from observational data will become increasingly important in data science with the increasing amount of available data, as a means of detecting potential triggers in epidemiology, social sciences, economy, biology, medicine, and other sciences.
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Editors and Affiliations
Bibliographic Information
Book Title: Cause Effect Pairs in Machine Learning
Editors: Isabelle Guyon, Alexander Statnikov, Berna Bakir Batu
Series Title: The Springer Series on Challenges in Machine Learning
DOI: https://doi.org/10.1007/978-3-030-21810-2
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer Nature Switzerland AG 2019
Hardcover ISBN: 978-3-030-21809-6Published: 05 November 2019
Softcover ISBN: 978-3-030-21812-6Published: 05 November 2020
eBook ISBN: 978-3-030-21810-2Published: 22 October 2019
Series ISSN: 2520-131X
Series E-ISSN: 2520-1328
Edition Number: 1
Number of Pages: XVI, 372
Number of Illustrations: 32 b/w illustrations, 90 illustrations in colour
Topics: Artificial Intelligence, Image Processing and Computer Vision, Pattern Recognition