Cause Effect Pairs in Machine Learning (Paperback, 1st ed. 2019)

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.

General

Imprint:	Springer Nature Switzerland AG
Country of origin:	Switzerland
Series:	The Springer Series on Challenges in Machine Learning
Release date:	November 2020
First published:	2019
Editors:	Isabelle Guyon • Alexander Statnikov • Berna Bakir Batu
Dimensions:	235 x 155mm (L x W)
Format:	Paperback
Pages:	372
Edition:	1st ed. 2019
ISBN-13:	978-3-03-021812-6
Categories:	Books > Computing & IT > Applications of computing > Pattern recognition Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning Books > Computing & IT > Applications of computing > Image processing > General
LSN:	3-03-021812-0
Barcode:	9783030218126

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