Elements of Causal Inference | Foundations and Learning Algorithms

Jonas Peters (u. a.)

ISBN 10: 0262037319 / ISBN 13: 9780262037310

Published by MIT Press, 2018

About this Item

Elements of Causal Inference | Foundations and Learning Algorithms | Jonas Peters (u. a.) | Buch | Einband - fest (Hardcover) | Englisch | 2018 | MIT Press | EAN 9780262037310 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

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Bibliographic Details

• Title: Elements of Causal Inference | Foundations and Learning Algorithms
• Publisher: MIT Press
• Publication Date: 2018
• Language: English
• ISBN 10: 0262037319
• ISBN 13: 9780262037310
• Binding: Buch
• Condition: Neu
• Dimensions: 236W x 182H x 21D millimeters 236 millimeters width by 182 millimeters height by 21 millimeters depth
• Weight: 708 grams
• Seller catalogs:
  • Bücher

About this title

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.

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.