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The Springer Series on Challenges in Machine Learning

Cause Effect Pairs in Machine Learning

Editors: Guyon, Isabelle, Statnikov, Alexander, Batu, Berna Bakir (Eds.)

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  • 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
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eBook $109.00
price for USA in USD (gross)
  • ISBN 978-3-030-21810-2
  • Digitally watermarked, DRM-free
  • Included format: EPUB, PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $139.99
price for USA in USD
  • ISBN 978-3-030-21809-6
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
About this book

This book presents ground-breaking advances in the domain of causal structure learning. The problem of distinguishing cause from effect (“Does altitude cause a change in atmospheric pressure, or vice versa?”) is here cast as a binary classification problem, to be tackled by machine learning algorithms.  Based on the results of the ChaLearn Cause-Effect Pairs Challenge, this book reveals that the joint distribution of two variables can be scrutinized by machine learning algorithms to reveal the possible existence of a “causal mechanism”, in the sense that the values of one variable may have been generated from the values of the other.  
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.


Table of contents (14 chapters)

Table of contents (14 chapters)

Buy this book

eBook $109.00
price for USA in USD (gross)
  • ISBN 978-3-030-21810-2
  • Digitally watermarked, DRM-free
  • Included format: EPUB, PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $139.99
price for USA in USD
  • ISBN 978-3-030-21809-6
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
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Bibliographic Information

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
Copyright
2019
Publisher
Springer International Publishing
Copyright Holder
Springer Nature Switzerland AG
eBook ISBN
978-3-030-21810-2
DOI
10.1007/978-3-030-21810-2
Hardcover ISBN
978-3-030-21809-6
Series ISSN
2520-131X
Edition Number
1
Number of Pages
XVI, 372
Number of Illustrations
32 b/w illustrations, 90 illustrations in colour
Topics