Elements of Causal Inference
Foundations and Learning Algorithms
By Bernhard Scholkopf, Dominik Janzing, Jonas Peters | Publisher: MIT Press
About the book
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.
Editions of Elements of Causal Inference
- Hardcover
- ISBN 9780262037310
- EBook
- ISBN 9780262344289
- EBook
- ISBN 9780262344296
Read an Excerpt
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.
Frequently Asked Questions
What is Elements of Causal Inference about?
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.
What core themes, tropes, or subjects are explored in Elements of Causal Inference?
Computing and Information Technology > Computer science > Artificial intelligence (AI)
Where can I read a sample of Elements of Causal Inference?
You can read an official preview of the few pages here https://www.book2look.com/book/9780262037310
Is Elements of Causal Inference part of a series, and can it be read as a standalone?
Yes it is a part of series Adaptive Computation and Machine Learning series
Who is/are the Author/s of the book Elements of Causal Inference?
Bernhard Scholkopf, Dominik Janzing, Jonas Peters
Who is the Publisher of the book Elements of Causal Inference?
MIT Press
What are the ISBN numbers for the physical and digital editions?
Elements of Causal Inference is available as hardcover(ISBN 9780262037310) and ebook(ISBN 9780262344289) and ebook(ISBN 9780262344296)
Where can I buy Elements of Causal Inference online or support local independent bookshops?
You can buy Elements of Causal Inference from below
Where can I buy Elements of Causal Inference?