Cause Effect Pairs in Machine Learning

Isabelle Guyon

ISBN 10: 3030218090 / ISBN 13: 9783030218096

Edité par Springer International Publishing, Springer Nature Switzerland, 2019

A propos de cet article

Druck auf Anfrage Neuware - Printed after ordering - 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 theChaLearn 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.

N° de réf. du vendeur 9783030218096

Détails bibliographiques

• Titre: Cause Effect Pairs in Machine Learning
• Éditeur: Springer International Publishing, Springer Nature Switzerland
• Date de parution: 2019
• Langue: anglais
• ISBN à 10 chiffres: 3030218090
• ISBN à 13 chiffres: 9783030218096
• Reliure: Buch
• État de l'article: Neu
• Dimensions: 241 L x 160 H x 27 P (en millimètres) 241 millimètres de largeur par 160 millimètres de hauteur par 27 millimètres de profondeur
• Poids: 746 grammes

A propos de ce titre

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.