Elements of Causal Inference: Foundations and Learning Algorithms (Adaptive Computation and Machine Learning series) by Jonas Peters (PDF)

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.

User’s Reviews

Reviews from Amazon users which were colected at the time this book was published on the website:

⭐The book is available for free from the publisher under a creative commons license, but this is a high-quality physical copy. The paper is nice (ordinary paper, not glossy like some textbooks). Figures are printed in color, which is great.In terms of the content, this book fills a very useful niche. There are many good books on causal inference, but most are written by and for traditional statisticians.This book, on the other hand, uses the concepts and notation of machine learning. The title seems to deliberately reference “Elements of Statistical Learning”, and if you are one of the many people who learned out of that book you will feel at home with this one.

⭐Great book!

⭐very good

⭐The contents is great to read as a starting point into causality. But the Kindle version is terrible. Some of symbols are displayed as ‘?’ on my Kindle 3 so I can’t read some of the notations.

⭐Terrible quality! The book cover is reversed and upside down. Will not buy from this dealer again.

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