Cause Effect Pairs in Machine Learning (The Springer Series on Challenges in Machine Learning) - Softcover

Book 7 of 8: The Springer Series on Challenges in Machine Learning
 
9783030218126: Cause Effect Pairs in Machine Learning (The Springer Series on Challenges in Machine Learning)
Softcover
ISBN 10: 3030218120 ISBN 13: 9783030218126
Publisher: Springer, 2020

Synopsis

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.


"synopsis" may belong to another edition of this title.

From the Back Cover

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.

"About this title" may belong to another edition of this title.

Publisher
Springer
Publication date
2020
Language
English
ISBN 10 
3030218120
ISBN 13 
9783030218126
Binding
Paperback
Edition number
1
Number of pages
388
Editor
Guyon Isabelle, Statnikov Alexander, Batu Berna Bakir

Other Popular Editions of the Same Title

9783030218096: Cause Effect Pairs in Machine Learning (The Springer Series on Challenges in Machine Learning)

Featured Edition

ISBN 10:  3030218090 ISBN 13:  9783030218096
Publisher: Springer, 2019
Hardcover