Causal Inference in Statistics: A Primer Pearl, Judea, Madelyn Glymour and Nicho

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ISBN: 1119186846 Item Width: 6.7 in Subject Area: Mathematics, Philosophy Publication Name: Causal Inference in Statistics : a Primer width: 6.7 in Item Height: 0.7 in Publikationsname: Causal Inference in Statistics: A Primer Language: English EAN: 9781119186847 Publication Year: 2016 Item Length: 9.5 in Format: Trade Paperback Number of Pages: 156 Pages Subject: Probability & Statistics / General, General Author: Madelyn Glymour, Judea Pearl, Nicholas P. Jewell Type: Textbook Item Weight: 10 Oz height: 0.7 in Publisher: Wiley & Sons, Incorporated, John Genre: Mathematik Ursprungsland: DE

Description

Causal Inference in Statistics: A Primer Pearl, Judea, Madelyn Glymour and Nicho. "Causal Inference in Statistics: A Primer" by Judea Pearl, Madelyn Glymour, and Nicholas P. Jewell is a comprehensive textbook on the subject of probability and statistics, focusing on the application of statistical methods to causal reasoning. The book, published by Wiley & Sons in 2016, covers topics such as general probability theory, general statistics, and causal inference. With a total of 156 pages, the trade paperback format makes it easily accessible for students and professionals in the field. The authors provide a clear and informative approach to understanding the complexities of causal inference in statistics, making it a valuable resource for those interested in this specialized area of mathematics and philosophy. "Causal Inference in Statistics: A Primer" by Judea Pearl, Madelyn Glymour, and Nicholas P. Jewell is a comprehensive textbook on the subject of probability and statistics, focusing on the application of statistical methods to causal reasoning. The book, published by Wiley & Sons in 2016, covers topics such as general probability theory, general statistics, and causal inference. With a total of 156 pages, the trade paperback format makes it easily accessible for students and professionals in the field. The authors provide a clear and informative approach to understanding the complexities of causal inference in statistics, making it a valuable resource for those interested in this specialized area of mathematics and philosophy.