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.
%0 Book
%1 PetersJanzingSchoelkopf17
%A Peters, Jonas
%A Janzing, Dominik
%A Schölkopf, Bernhard
%B Adaptive Computation and Machine Learning
%C Cambridge, MA
%D 2017
%I MIT Press
%K 01801 101 book shelf mitpress numerical ai knowledge processing data pattern recognition analysis learn algorithm
%T Elements of Causal Inference: Foundations and Learning Algorithms
%U https://mitpress.mit.edu/books/elements-causal-inference
%X 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.
%@ 978-0-262-03731-0
@book{PetersJanzingSchoelkopf17,
abstract = {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.},
added-at = {2018-04-15T19:08:44.000+0200},
address = {Cambridge, MA},
author = {Peters, Jonas and Janzing, Dominik and Sch{\"o}lkopf, Bernhard},
biburl = {https://www.bibsonomy.org/bibtex/2a38bf503b49180cdd113b4480e384c4f/flint63},
file = {eBook:2017/PetersJanzingSchoelkopf17.pdf:PDF;MIT Press Product Page:https\://mitpress.mit.edu/books/elements-causal-inference:URL;Amazon Search inside:http\://www.amazon.de/gp/reader/ISBN10/:URL},
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intrahash = {a38bf503b49180cdd113b4480e384c4f},
isbn = {978-0-262-03731-0},
keywords = {01801 101 book shelf mitpress numerical ai knowledge processing data pattern recognition analysis learn algorithm},
lccn = {https://lccn.loc.gov/2017020087},
publisher = {MIT Press},
series = {Adaptive Computation and Machine Learning},
timestamp = {2018-04-16T11:31:45.000+0200},
title = {Elements of Causal Inference: Foundations and Learning Algorithms},
url = {https://mitpress.mit.edu/books/elements-causal-inference},
username = {flint63},
year = 2017
}