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Causal Inference in Statistics: A Primer (2016) by Judea Pearl [Dr Soc]

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  Judea Pearl, "Causal Inference in
Statistics: A Primer" 
  English | ISBN: 1119186846 | 2016 | 150 pages | True PDF | 1 MB
  
 
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.
  
 
Causal Inference in Statistics fills that gap. Using simple examples and plain language, the book lays out how to define
causal parameters; the assumptions necessary to estimate causal parameters in a variety of situations; how to express
those assumptions mathematically; whether those assumptions have testable implications; how to predict the effects of
interventions; and how to reason counterfactually. These are the foundational tools that any student of statistics needs
to acquire in order to use statistical methods to answer causal questions of interest.
  
  This book is
accessible to anyone with an interest in interpreting data, from undergraduates, professors, researchers, or to the
interested layperson. Examples are drawn from a wide variety of fields, including medicine, public policy, and law; 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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