Causal Inference in Statistics: A Primer

Causal Inference in Statistics: A Primer

Many of the concepts and terminology surrounding modern causal inference can be quite intimidating to the novice. Judea Pearl presents a book ideal for beginners in statistics, providing a comprehensive introduction to the field of causality.  Examples from classical statistics are presented throughout to demonstrate the need for causality in resolving decision-making dilemmas posed by data. Causal methods are also compared to traditional statistical methods, whilst questions are provided at the end of each section to aid student learning.

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Editorial Reviews

Review

A few high-quality books on the topic of causality inference have recently been published, but this book fills an important gap by providing a simple and clear primer. All introductory courses of statistics should include a discussion of the basic ideas in causality inference. The book could be used as a companion to the lectures. There are mathematical reviews on mathSciNet. April 2017)

About the Author

Judea Pearl is Professor of Computer Science and Statistics at the University of California, Los Angeles, where he directs the Cognitive Systems Laboratory and conducts research in artificial intelligence, causal inference and philosophy of science. He is a Co-Founder and Editor of the Journal of Causal Inference and the author of three landmark books in inference-related areas. His latest book, Causality: Models, Reasoning and Inference (Cambridge, 2000, 2009), has introduced many of the methods used in modern causal analysis. It  won the Lakatosh Award from the London School of Economics and is cited by more than 10,000 scientific publications.

Pearl is a member of the National Academy of Sciences, the National Academy of Engineering, and the Association for Artificial Intelligence. He is a recipient of many awards, including the Harvey Prize and the Alan Turing Award.

The person is Madelyn Glymour. He is a data analyst at Carnegie Mellon University and a science writer and editor at UCLA. She is interested in making complex concepts accessible to broad audiences.

Nicholas P. He is a professor at the University of California, Berkeley. He has held various academic and administrative positions at Berkeley since he arrived in 1981 He held appointments at the University of Edinburgh, Oxford University, the London School of Hygiene and Tropical Medicine, and the University of Kyoto. He was a Fellow at the Rockefeller Foundation in Italy in 2007.

He is a Fellow of the American Statistical Association, the Institute of Mathematical Statistics, and the American Association for the advancement of science. He received the Marvin Zelen Leadership Award in Statistical Science from Harvard University. He is the editor. The American Statistical Association has a journal. Chair of the Statistics Section. His research focuses on the application of statistical methods to infectious and chronic disease epidemiology, the assessment of drug safety, time-to-event analyses, and human rights. There are videos.

What is the introduction to Causality? Part I is with professors Judea Pearl and Nicholas P. Jewell.

Teaching Causality? Part I with professors Judea Pearl and Rob Gould.

In order to access Part II for each of these videos, please register with Statistics Views for free

From the Back Cover

Causal inference in statistics is a primer.

Judea Pearl is a computer scientist at the University of California Los Angeles.

Carnegie Mellon University, Pittsburgh, USA has a philosophy student named Madelyn Glymour.

and

Nicholas P. Jewell is a biostatistician at the University of California, Berkeley.

Causality is important to understanding and using data. Even though hundreds of introductory texts are available on statistical methods of data analysis, no beginner-level book has been written about the exploding arsenal.

Causal inference in statistics. That gap is filled. Using simple examples and plain language, the book shows how to define causality parameters, how to estimate causality in a variety of situations, and how to predict the effects of interventions. In order to use statistical methods to answer questions of interest, a student of statistics needs to acquire these tools.

Anyone with an interest in interpreting data can read this book. A brief introduction to probability and statistics is provided for the uninitiated, and each chapter comes with study questions to reinforce the readers understanding.

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Best Sellers Rank

#156,742 in Kindle Store (See Top 100 in Kindle Store) #36 in Probability & Statistics (Kindle Store) #202 in Science History & Philosophy #323 in Probability & Statistics (Books)

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