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Algorithms (Adaptive Computation and Machine Learning
series)
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Elements of Causal Inference: Foundations and Learning
Algorithms (Adaptive Computation and Machine Learning
series)
BOOK DETAIL
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Series: Adaptive Computation and Machine Learning series Hardcover: 288 pages Publisher: The MIT Press
(November 29, 2017) Language: English ISBN-10: 0262037319 ISBN-13: 978-0262037310 Product
Dimensions: 9 x 7.2 x 0.9 inches
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Book 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.

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