We specialize in hard to find items
All Categories
Elements of Causal Inference: Foundations and Learning Algorithms (Adaptive Computation and Machine Learning series)
Share Tweet
*Price and Stocks may change without prior notice
*Packaging of actual item may differ from photo shown
- Electrical items MAY be 110 volts.
- 7 Day Return Policy
- All products are genuine and original
About Elements Of Causal Inference: Foundations And
Product Description 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. Review 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 About the Author Jonas Peters is Professor of Statistics at the University of Copenhagen. Dominik Janzing is a Senior Research Scientist at the Max Planck Institute for Intelligent Systems in Tü bingen, 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.
