Cause Effect Pairs in Machine Learning

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Beschreibung

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.

Zusammenfassung

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

Inhaltsverzeichnis

1. The cause-effect problem: motivation, ideas, and popular misconceptions.- 2. Evaluation methods of cause-effect pairs.- 3. Learning Bivariate Functional Causal Models.- 4. Discriminant Learning Machines.- 5. Cause-Effect Pairs in Time Series with a Focus on Econometrics.- 6. Beyond cause-effect pairs.- 7. Results of the Cause-Effect Pair Challenge.- 8. Non-linear Causal Inference using Gaussianity Measures.- 9. From Dependency to Causality: A Machine Learning Approach.- 10. Pattern-based Causal Feature Extraction.- 11. Training Gradient Boosting Machines using Curve-fitting and Information-theoretic Features for Causal Direction Detection.- 12. Conditional distribution variability measures for causality detection.- 13. Feature importance in causal inference for numerical and categorical variables.- 14. Markov Blanket Ranking using Kernel-based Conditional Dependence Measures.

Details

Erscheinungsjahr:	2020
Genre:	Informatik
Rubrik:	Naturwissenschaften & Technik
Medium:	Taschenbuch
Reihe:	The Springer Series on Challenges in Machine Learning
Inhalt:	xvi 372 S. 32 s/w Illustr. 90 farbige Illustr. 372 p. 122 illus. 90 illus. in color.
ISBN-13:	9783030218126
ISBN-10:	3030218120
Sprache:	Englisch
Ausstattung / Beilage:	Paperback
Einband:	Kartoniert / Broschiert
Redaktion:	Guyon, Isabelle Batu, Berna Bakir Statnikov, Alexander
Herausgeber:	Isabelle Guyon/Alexander Statnikov/Berna Bakir Batu
Auflage:	1st ed. 2019
Hersteller:	Springer International Publishing Springer International Publishing AG The Springer Series on Challenges in Machine Learning
Maße:	235 x 155 x 21 mm
Von/Mit:	Isabelle Guyon (u. a.)
Erscheinungsdatum:	05.11.2020
Gewicht:	0,587 kg