Cause Effect Pairs in Machine Learning
Aus der Reihe
The Springer Series on Challenges in Machine Learning
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- Hardcover ausgewählt
- Taschenbuch
- eBook
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Sprache:Englisch
Fr. 182.00
inkl. gesetzl. MwSt.,
Beschreibung
Produktdetails
Einband
Gebundene Ausgabe
Erscheinungsdatum
05.11.2019
Abbildungen
XVI, 122 illus., 90 illus. in color., farbige Illustrationen, schwarz-weiss Illustrationen
Herausgeber
Isabelle Guyon + weitereVerlag
SpringerSeitenzahl
372
Maße (L/B/H)
24.1/16/2.7 cm
Gewicht
734 g
Auflage
1st ed. 2019
Sprache
Englisch
ISBN
978-3-030-21809-6
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.
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.
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