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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...
About the Author
Jonas Peters is Associate Professor of Statistics at the University of Copenhagen.
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...
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- PublisherThe MIT Press
- Publication date2017
- ISBN 10 0262037319
- ISBN 13 9780262037310
- BindingHardcover
- LanguageEnglish
- Number of pages288
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