Elements of Causal Inference: Foundations and Learning Algorithms

(Part of the Adaptive Computation and Machine Learning Series)

by Jonas Peters, Bernhard Scholkopf, Dominik Janzing, et al.

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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...

Format:Hardcover

Language:English

ISBN:0262037319

ISBN13:9780262037310

Release Date:November 2017

Publisher:MIT Press

Length:288 Pages

Weight:1.55 lbs.

Dimensions:0.9" x 7.2" x 9.0"

Age Range:18 years and up

Grade Range:Postsecondary and higher

Related Subjects

Computers Computers & Technology

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Elements of Causal Inference: Foundations and Learning Algorithms

(Part of the Adaptive Computation and Machine Learning Series)

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