Elements of Causal Inference: Foundations and Learning Algorithms (Adaptive Computation and Machine Learning series)
Peters, Jonas; Janzing, Dominik; Scholkopf, Bernhard
Language: English
Published by The MIT Press, 2017
- Hardcover
- New
Seller: Ria Christie Collections, Uxbridge, United KingdomRia Christie Collections
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In English.
Seller Inventory # ria9780262037310_new
- Title
- Elements of Causal Inference: Foundations and Learning Algorithms (Adaptive Computation and Machine Learning series)
- Author
- Peters, Jonas; Janzing, Dominik; Scholkopf, Bernhard
- Publisher
- The MIT Press
- Publication year
- 2017
- Condition
- New
- Binding
- Hardcover
- Language
- English
- ISBN 10
- 0262037319
- ISBN 13
- 9780262037310
- Item weight
- 844 grams
The mathematization of causality is a relatively recent development, and has become increasingly important in data science and machine learning. This book offers a self-contained and concise introduction to causal models and how to learn them from data.
After explaining the need for causal models and discussing some of the principles underlying causal inference, the book teaches readers how to use causal models: how to compute intervention distributions, how to infer causal models from observational and interventional data, and how causal ideas could be exploited for classical machine learning problems. All of these topics are discussed first in terms of two variables and then in the more general multivariate case. The bivariate case turns out to be a particularly hard problem for causal learning because there are no conditional independences as used by classical methods for solving multivariate cases. The authors consider analyzing statistical asymmetries between cause and effect to be highly instructive, and they report on their decade of intensive research into this problem.
The book is accessible to readers with a background in machine learning or statistics, and can be used in graduate courses or as a reference for researchers. The text includes code snippets that can be copied and pasted, exercises, and an appendix with a summary of the most important technical concepts.
"Synopsis" may belong to another edition of this title.
About the Author
Dominik Janzing is a Senior Research Scientist at the Max Planck Institute for Intelligent Systems in Tübingen, Germany.
Bernhard Schölkopf is Director at the Max Planck Institute for Intelligent Systems in Tübingen, Germany. He is coauthor of Learning with Kernels (2002) and is a coeditor of Advances in Kernel Methods: Support Vector Learning (1998), Advances in Large-Margin Classifiers (2000), and Kernel Methods in Computational Biology (2004), all published by the MIT Press.
"About the title" may belong to another edition of this title.
Ria Christie Collections
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