Items related to Elements of Causal Inference: Foundations and Learning...
Elements of Causal Inference: Foundations and Learning Algorithms (Adaptive Computation and Machine Learning series) - Hardcover
Synopsis
A concise and self-contained introduction to causal inference, increasingly important in data science and machine learning.
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
Jonas Peters is Associate Professor of Statistics at the University of Copenhagen.
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 this title" may belong to another edition of this title.
Search results for Elements of Causal Inference: Foundations and Learning...
Stock Image
Elements of Causal Inference: Foundations and Learning Algorithms (Adaptive Computation and Machine Learning series)
Published by
The MIT Press (edition ), 2017
ISBN 10: 0262037319
ISBN 13: 9780262037310
Used
Hardcover
Seller: BooksRun, Philadelphia, PA, U.S.A.
Seller rating 5 out of 5 stars
Hardcover. Condition: Very Good. It's a well-cared-for item that has seen limited use. The item may show minor signs of wear. All the text is legible, with all pages included. It may have slight markings and/or highlighting. Seller Inventory # 0262037319-11-1
Stock Image
Elements of Causal Inference: Foundations and Learning Algorithms (Adaptive Computation and Machine Learning series)
Seller: World of Books (was SecondSale), Montgomery, IL, U.S.A.
Seller rating 5 out of 5 stars
Condition: Very Good. Item in very good condition! Textbooks may not include supplemental items i.e. CDs, access codes etc. Seller Inventory # 00109596150
Stock Image
Elements of Causal Inference: Foundations and Learning Algorithms (Adaptive Computation and Machine Learning series)
Published by
The MIT Press (edition ), 2017
ISBN 10: 0262037319
ISBN 13: 9780262037310
Used
Hardcover
Seller: BooksRun, Philadelphia, PA, U.S.A.
Seller rating 5 out of 5 stars
Hardcover. Condition: Very Good. It's a well-cared-for item that has seen limited use. The item may show minor signs of wear. All the text is legible, with all pages included. It may have slight markings and/or highlighting. Seller Inventory # 0262037319-8-1
Stock Image
Elements of Causal Inference: Foundations and Learning Algorithms (Adaptive Computation and Machine Learning series)
Seller: Bellwetherbooks, McKeesport, PA, U.S.A.
Seller rating 4 out of 5 stars
hardcover. Condition: Good. Bruise/tear to cover. Seller Inventory # mon0000054046
Stock Image
Elements of Causal Inference: Foundations and Learning Algorithms (Adaptive Computation and Machine Learning series)
Seller: HPB-Red, Dallas, TX, U.S.A.
Seller rating 5 out of 5 stars
hardcover. Condition: Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority! Seller Inventory # S_433633505
Stock Image
Elements of Causal Inference: Foundations and Learning Algorithms
Seller: World of Books Inc, Montgomery, IL, U.S.A.
Seller rating 2 out of 5 stars
Hardback. Condition: Very Good. A concise and self-contained introduction to causal inference, increasingly important in data science and machine learning. 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. Seller Inventory # CIN0262037319VG
Stock Image
Elements of Causal Inference : Foundations and Learning Algorithms
Seller: Better World Books, Mishawaka, IN, U.S.A.
Seller rating 5 out of 5 stars
Condition: Good. Pages intact with minimal writing/highlighting. The binding may be loose and creased. Dust jackets/supplements are not included. Stock photo provided. Product includes identifying sticker. Better World Books: Buy Books. Do Good. Seller Inventory # 15763804-6
Stock Image
Elements of Causal Inference: Foundations and Learning Algorithms (Adaptive Computation and Machine Learning series)
Seller: Books From California, Simi Valley, CA, U.S.A.
Seller rating 4 out of 5 stars
hardcover. Condition: Fine. Seller Inventory # mon0004222851
Stock Image
Elements of Causal Inference: Foundations and Learning Algorithms (Adaptive Computation and Machine Learning series)
Seller: thebookforest.com, San Rafael, CA, U.S.A.
Seller rating 4 out of 5 stars
Condition: New. Seller Inventory # BAY07-00141
Stock Image
Elements of Causal Inference: Foundations and Learning Algorithms (Adaptive Computation and Machine Learning series)
Seller: Books Puddle, New York, NY, U.S.A.
Seller rating 4 out of 5 stars
Condition: New. Seller Inventory # 26375629929