Elements of Causal Inference: Foundations and Learning Algorithms (Adaptive Computation and Machine Learning series)
| Author | : | |
| Rating | : | 4.51 (991 Votes) |
| Asin | : | 0262037319 |
| Format Type | : | paperback |
| Number of Pages | : | 288 Pages |
| Publish Date | : | 2015-03-22 |
| Language | : | English |
DESCRIPTION:
. 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, GermanyHe is coauthor of Learning with Kernels (2002) and is a coeditor of Advances in Kernel Methods: Support Vector Learning (1998), Advances in Large-M
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, GermanyHe is coauthor of
The mathematization of causality is a relatively recent development, and has become increasingly important in data science and machine learning. 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. This book offers a self-contained and concise introduction to causal models and how to learn them from data. 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.. 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. 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