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Elements of Causal Inference - Foundations and Learning Algorithms (Hardcover)
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Elements of Causal Inference - Foundations and Learning Algorithms (Hardcover)
Series: Elements of Causal Inference
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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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