Cause Effect Pairs in Machine Learning
Editors: Guyon, Isabelle, Statnikov, Alexander, Batu, Berna Bakir (Eds.)
Free Preview- 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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- About this book
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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)
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The Cause-Effect Problem: Motivation, Ideas, and Popular Misconceptions
Pages 3-26
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Evaluation Methods of Cause-Effect Pairs
Pages 27-99
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Learning Bivariate Functional Causal Models
Pages 101-153
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Discriminant Learning Machines
Pages 155-189
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Cause-Effect Pairs in Time Series with a Focus on Econometrics
Pages 191-214
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Table of contents (14 chapters)
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Bibliographic Information
- Bibliographic Information
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- Book Title
- Cause Effect Pairs in Machine Learning
- Editors
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- 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
- Softcover ISBN
- 978-3-030-21812-6
- Series ISSN
- 2520-131X
- Edition Number
- 1
- Number of Pages
- XVI, 372
- Number of Illustrations
- 32 b/w illustrations, 90 illustrations in colour
- Topics
