Elements of Causal Inference: Foundations and Learning Algorithms

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Jonas Peters (Associate Professor of Statistics, University of Copenhagen) Dominik Janzing (Senior Research Scientist, Max Planck Institute for Intelligent Systems)

Elements of Causal Inference

Foundations and Learning Algorithms

Jonas Peters (Associate Professor of Statistics, University of Copenhagen), Dominik Janzing (Senior Research Scientist, Max Planck Institute for Intelligent Systems), Bernhard Schölkopf (Director of the Max Planck Institute for Intelligent in Tübingen, Germany, Professor for Machine Lea, Max Planck Institute for Intelligent Systems)

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English

MIT Press
29 November 2017

PDA & Handheld programming; Machine learning; Neural networks & fuzzy systems

Series: Adaptive Computation and Machine Learning series

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.

By: Jonas Peters (Associate Professor of Statistics University of Copenhagen), Dominik Janzing (Senior Research Scientist, Max Planck Institute for Intelligent Systems), Bernhard Schölkopf (Director of the Max Planck Institute for Intelligent in Tübingen, Germany, Professor for Machine Lea, Max Planck Institute for Intelligent Systems)
Imprint: MIT Press
Country of Publication: United States
Dimensions: Height: 229mm, Width: 178mm, Spine: 16mm
ISBN: 9780262037310
ISBN 10: 0262037319
Series: Adaptive Computation and Machine Learning series
Pages: 288
Publication Date: 29 November 2017
Recommended Age: From 18 years
Audience: College/higher education , Primary
Format: Hardback
Publisher's Status: Active

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 T bingen, Germany. Bernhard Sch lkopf is Director at the Max Planck Institute for Intelligent Systems in T bingen, 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.