Elements of Causal Inference
foundations and learning algorithms
$129.64
- Hardcover
288 pages
- Release Date
28 November 2017
Summary
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 t…
Book Details
| ISBN-13: | 9780262037310 |
|---|---|
| ISBN-10: | 0262037319 |
| Author: | Jonas Peters, Dominik Janzing, Bernhard Schölkopf |
| Publisher: | MIT Press Ltd |
| Imprint: | MIT Press |
| Format: | Hardcover |
| Number of Pages: | 288 |
| Release Date: | 28 November 2017 |
| Weight: | 712g |
| Dimensions: | 229mm x 178mm x 16mm |
| Series: | Adaptive Computation and Machine Learning series |
About The Author
Jonas Peters
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.
Returns
This item is eligible for free returns within 30 days of delivery. See our returns policy for further details.