Elements of Causal Inference: Foundations and Learning Algorithms by Jonas Peter

by Jonas Peter

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The Nile on eBay FREE SHIPPING UK WIDE Elements of Causal Inference by Jonas Peters, Dominik Janzing, Bernhard Sch lkopf A concise and self-contained introduction to causal inference, increasingly important in data science and machine learning. FORMAT Hardcover LANGUAGE English CONDITION Brand New Publisher Description A concise and self-contained introduction to causal inference, increasingly important in data science and machine 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 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

• Binding: Hardcover
• ISBN: 9780262037310
• Condition: Fine

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