PDF Ebook Elements of Causal Inference: Foundations and Learning Algorithms (Adaptive Computation and Machine Learning series), by Jonas Peters
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Elements of Causal Inference: Foundations and Learning Algorithms (Adaptive Computation and Machine Learning series), by Jonas Peters
PDF Ebook Elements of Causal Inference: Foundations and Learning Algorithms (Adaptive Computation and Machine Learning series), by Jonas Peters
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Review
Elements of Causal Inference is an important contribution to the growing literature on causal analysis. This lucid monograph elegantly weaves together statistics, machine learning, and causality to provide a holistic picture of how we and machines can use data to understand the world.―David Blei, Professor of Computer Science and Statistics, Columbia UniversityCausal inference is a well-established field in statistics, but it is still relatively underdeveloped within machine learning. This is partly due to the lack of good learning resources before Elements of Causal Inference came along. This book is high-quality work that breaks through, firmly establishing a connection between causal inference and general machine learning.―Ricardo Silva, Senior Lecturer, University College London; Turing Fellow, Alan Turing Institute
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About the Author
Jonas Peters is Associate Professor of Statistics at the University of Copenhagen.
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Product details
Series: Adaptive Computation and Machine Learning series
Hardcover: 288 pages
Publisher: The MIT Press (November 29, 2017)
Language: English
ISBN-10: 0262037319
ISBN-13: 978-0262037310
Product Dimensions:
7 x 0.9 x 9 inches
Shipping Weight: 1.6 pounds (View shipping rates and policies)
Average Customer Review:
4.0 out of 5 stars
2 customer reviews
Amazon Best Sellers Rank:
#284,384 in Books (See Top 100 in Books)
The book is available for free from the publisher under a creative commons license, but this is a high-quality physical copy. The paper is nice (ordinary paper, not glossy like some textbooks). Figures are printed in color, which is great.In terms of the content, this book fills a very useful niche. There are many good books on causal inference, but most are written by and for traditional statisticians.This book, on the other hand, uses the concepts and notation of machine learning. The title seems to deliberately reference “Elements of Statistical Learningâ€, and if you are one of the many people who learned out of that book you will feel at home with this one.
This is an important book summarizing the author's research on causal discovery, and this is the only book you will find such a broad treatment of the subject. However, many parts of the book still look like a draft. Looking forward for the next iterations of the book.
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