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Causal Inference in Statistics - a Primer

Causal Inference in Statistics - a Primer

A Primer

by PearlJudea Pearl Nicholas P. Jewell and others
Paperback
Publication Date: 04/03/2016

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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.
ISBN:
9781119186847
9781119186847
Category:
Mathematics
Format:
Paperback
Publication Date:
04-03-2016
Publisher:
John Wiley & Sons (UK)
Pages:
160
Dimensions (mm):
2440x1700mm
Weight:
0kg
Judea Pearl

Judea Pearl is a world-renowned Israeli-American computer scientist and philosopher, known for his world-leading work in AI and the development of Bayesian networks, as well as his theory of causal and counterfactual inference. In 2011, he won the most prestigious award in computer science, the Alan Turing Award.

He has also received the Rumelhart Prize (Cognitive Science Society), the Benjamin Franklin Medal (Franklin Institute) and the Lakatos Award (London School of Economics), and he is the founder and president of the Daniel Pearl Foundation.

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