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Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction by Guido W Imbens
135.00 SAR

Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction by Guido W Imbens

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Category Type
General
Author
Guido W Imbens
Publisher
Cambridge University Press 
Book Language
English
Description:

Most questions in social and biomedical sciences are causal in nature: what would happen to individuals, or to groups, if part of their environment were changed? In this groundbreaking text, two world-renowned experts present statistical methods for studying such questions. This book starts with the notion of potential outcomes, each corresponding to the outcome that would be realized if a ...

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    Category Type
    General
    Item EAN
    2724327618127
    People
    Author
    Guido W Imbens
    People
    Publisher
    Cambridge University Press 
    Languages and countries
    Book Language
    English
    Category Type
    General
    Item EAN
    2724327618127
    People
    Author
    Guido W Imbens
    People
    Publisher
    Cambridge University Press 
    Languages and countries
    Book Language
    English
    Read more
  •  

    Description:

    Most questions in social and biomedical sciences are causal in nature: what would happen to individuals, or to groups, if part of their environment were changed? In this groundbreaking text, two world-renowned experts present statistical methods for studying such questions. This book starts with

    Most questions in social and biomedical sciences are causal in nature: what would happen to individuals, or to groups, if part of their environment were changed? In this groundbreaking text, two world-renowned experts present statistical methods for studying such questions. This book starts with the notion of potential outcomes, each corresponding to the outcome that would be realized if a subject were exposed to a particular treatment or regime. In this approach, causal effects are comparisons of such potential outcomes. The fundamental problem of causal inference is that we can only observe one of the potential outcomes for a particular subject. The authors discuss how randomized experiments allow us to assess causal effects and then turn to observational studies. They lay out the assumptions needed for causal inference and describe the leading analysis methods, including, matching, propensity-score methods, and instrumental variables. Many detailed applications are included, with special focus on practical aspects for the empirical researcher

 

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