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

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

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.