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

Libro 7 de 8: The Springer Series on Challenges in Machine Learning

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9783030218096: Cause Effect Pairs in Machine Learning (The Springer Series on Challenges in Machine Learning)
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ISBN 10: 3030218090 ISBN 13: 9783030218096
Editorial: Springer, 2019

Sinopsis

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.


"Sinopsis" puede pertenecer a otra edición de este libro.

De la contraportada

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.

"Sobre este título" puede pertenecer a otra edición de este libro.

Editorial
Springer
Año de publicación
2019
Idioma
Inglés
ISBN 10 
3030218090
ISBN 13 
9783030218096
Encuadernación
Tapa dura
Número de edición
1
Número de páginas
388
Contacto del fabricante
no disponible
Persona responsable
CMN Printpool Inh. Mathis Neumann
https://www.also.com/ec/cms5/en_6000/6000/index.jsp

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Hamburg
Alemania

Otras ediciones populares con el mismo título

9783030218126: Cause Effect Pairs in Machine Learning (The Springer Series on Challenges in Machine Learning)

Edición Destacada

ISBN 10:  3030218120 ISBN 13:  9783030218126
Editorial: Springer, 2020
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