Mobile Phones, Tablets & E-Readers - Elements of Causal Inference: Foundations and Learning 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)

Name: Elements of Causal Inference: Foundations and Learning Algorithms (Adaptive Computation and Machine Learning series)
Price: 45 USD
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4.7 of 5 Stars: Mobile Phones, Tablets & E-Readers - Elements of Causal Inference: Foundations and Learning Algorithms (Adaptive Computation and Machine Learning series)

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Book Synopsis: 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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Looking to enhance your data science and machine learning skills? Look no further! Introducing "Elements of Causal Inference: Foundations and Learning Algorithms" - an essential read for anyone interested in the mathematization of causality. With the growing importance of causal inference in the field, this self-contained and concise introduction offers the perfect blend of theoretical foundations and practical applications.

With this book, you will gain a solid understanding of causal models and how to learn them from data. Discover how to compute intervention distributions, infer causal models from observational and interventional data, and leverage causal ideas for classical machine learning problems. Whether you're a seasoned professional or just starting out, the comprehensive coverage of both bivariate and multivariate cases makes this an invaluable resource.

What sets this book apart is its focus on analyzing statistical asymmetries between cause and effect. Backed by a decade of intensive research, the authors provide deep insights into this challenging problem. The book is thoughtfully structured, ensuring accessibility for readers with a background in machine learning or statistics. Whether you're pursuing graduate studies or conducting advanced research, "Elements of Causal Inference" is an indispensable companion.

Don't miss out on this opportunity to expand your knowledge and master the art of causal inference. Order your copy of "Elements of Causal Inference: Foundations and Learning Algorithms" today and unlock the secrets of data science's latest frontier.

Ready to take your data science and machine learning skills to the next level? Order now!

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