Causal inference in statistics: A primer

By:

Pearl, Judea

Contributor(s):

Material type:

TextPublication details: United Kingdom John Wiley & Sons, Inc. 2016Description: xvii, 136 p. Includes bibliographical reference and indexISBN:

9781119186847

Other classification:

Summary: Causality is central to the understanding and use of data. Without an understanding of cause–effect relationships, we cannot use data to answer questions as basic as "Does this treatment harm or help patients?" But though hundreds of introductory texts are available on statistical methods of data analysis, until now, no beginner-level book has been written about the exploding arsenal of methods that can tease causal information from data.

Item type:

Textual

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Cover image	Item type	Current library	Home library	Collection	Shelving location	Call number	Materials specified	Vol info	URL	Copy number	Status	Notes	Date due	Barcode	Item holds	Item hold queue priority	Course reserves
	Textual	Ratan Tata Library	Ratan Tata Library								Available			RT1528705

Causality is central to the understanding and use of data. Without an understanding of cause–effect relationships, we cannot use data to answer questions as basic as "Does this treatment harm or help patients?" But though hundreds of introductory texts are available on statistical methods of data analysis, until now, no beginner-level book has been written about the exploding arsenal of methods that can tease causal information from data.

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