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Cause Effect Pairs in Machine Learning 2019 ed. [Pehme köide]

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  • Formaat: Paperback / softback, 372 pages, kõrgus x laius: 235x155 mm, kaal: 593 g, 90 Illustrations, color; 32 Illustrations, black and white; XVI, 372 p. 122 illus., 90 illus. in color., 1 Paperback / softback
  • Sari: The Springer Series on Challenges in Machine Learning
  • Ilmumisaeg: 05-Nov-2020
  • Kirjastus: Springer Nature Switzerland AG
  • ISBN-10: 3030218120
  • ISBN-13: 9783030218126
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Cause Effect Pairs in Machine Learning 2019 ed.Suurem pilt
  • Formaat: Paperback / softback, 372 pages, kõrgus x laius: 235x155 mm, kaal: 593 g, 90 Illustrations, color; 32 Illustrations, black and white; XVI, 372 p. 122 illus., 90 illus. in color., 1 Paperback / softback
  • Sari: The Springer Series on Challenges in Machine Learning
  • Ilmumisaeg: 05-Nov-2020
  • Kirjastus: Springer Nature Switzerland AG
  • ISBN-10: 3030218120
  • ISBN-13: 9783030218126
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9783030218126.html
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.


Arvustused

The book can be recommended for researchers in causal discovery with expertise in either statistics or machine learning. Although the chapters are written by different authors, readers will appreciate the book's coherent organization ... . (Corrado Mencar, Computing Reviews, May 17, 2022)

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.