Elements of Causal Inference: Foundations and Learning Algorithms

Elements of Causal Inference: Foundations and Learning Algorithms

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Product Details

ISBN-13: 9780262037310
Publisher: MIT Press
Publication date: 11/29/2017
Series: Adaptive Computation and Machine Learning series
Pages: 288
Sales rank: 676,301
Product dimensions: 7.10(w) x 9.10(h) x 1.00(d)
Age Range: 18 Years

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.

What People are Saying About This

Ricardo Silva

Causal inference is a well-established field in statistics, but it is still relatively underdeveloped within machine learning. This is partly due to the lack of good learning resources before Elements of Causal Inference came along. This book is high-quality work that breaks through, firmly establishing a connection between causal inference and general machine learning.

From the Publisher

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 University

Causal inference is a well-established field in statistics, but it is still relatively underdeveloped within machine learning. This is partly due to the lack of good learning resources before Elements of Causal Inference came along. This book is high-quality work that breaks through, firmly establishing a connection between causal inference and general machine learning.

Ricardo Silva, Senior Lecturer, University College London; Turing Fellow, Alan Turing Institute

Endorsement

Causal inference is a well-established field in statistics, but it is still relatively underdeveloped within machine learning. This is partly due to the lack of good learning resources before Elements of Causal Inference came along. This book is high-quality work that breaks through, firmly establishing a connection between causal inference and general machine learning.

Ricardo Silva, Senior Lecturer, University College London; Turing Fellow, Alan Turing Institute

David Blei

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.

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