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Cause Effect Pairs in Machine Learning

Name: Cause Effect Pairs in Machine Learning
Brand: Springer Nature Switzerland AG
SKU: 9783030218126
Price: 103.25 EUR
Availability: InStock

Автор: Berna Bakir Batu, Alexander Statnikov

Език:

Корици: С меки корици

Издател: Springer Nature Switzerland AG

Наличност: Външен склад

Изпращаме след 5-8 дни

103.25 € 201.94 лв

This book presents ground-breaking advances in the domain of causal structure learning. The problem...

Информация за книгата

Автор

Berna Bakir Batu, Alexander Statnikov

Език

Английски език

Корици

Книга - С меки корици

Издадена

2020

страници

388

EAN

9783030218126

ISBN

3030218120

Enbook ID

33377234

Издател

Springer Nature Switzerland AG

Теглоt

587

Размери

155 x 235 x 21

Пълно описание

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.