TY  - BOOK
AU  - Peters, Jonas
AU  - Janzing, Dominik
AU  - Schölkopf, Bernhard
AB  - 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.
ID  - OAPEN ID: 1004045
ID  - OAPEN ID: OCN: 1100492112
KW  - Causality
KW  - machine learning
KW  - statistical models
KW  - probability theory
KW  - statistics
KW  - assumptions
KW  - cause-effect models
KW  - interventions
KW  - counterfactuals
KW  - SCMs
KW  - cause-effect models
KW  - identifiability
KW  - semi-supervised learning
KW  - covariate shift
KW  - multivariate causal models
KW  - markov
KW  - faithfulness
KW  - causal minimality
KW  - do-calculus
KW  - falsifiability
KW  - potential outcomes
KW  - algorithmic independence
KW  - half-sibling regression
KW  - episodic reinforcement learning
KW  - domain adaptation
KW  - simpson's paradox
KW  - conditional independence
KW  - computer science
L1  - https://library.oapen.org/bitstream/id/056a11be-ce3a-44b9-8987-a6c68fce8d9b/11283.pdf
LA  - English
LK  - http://library.oapen.org/handle/20.500.12657/26040
PB  - The MIT Press
PP  - Cambridge
PY  - 2017
SN  - 9780262037310
TI  - Elements of Causal Inference : Foundations and Learning Algorithms
ER  - 
