The Resource Causal inference for statistics, social, and biomedical sciences : an introduction, Guido W. Imbens & Donald B. Rubin
Causal inference for statistics, social, and biomedical sciences : an introduction, Guido W. Imbens & Donald B. Rubin
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The item Causal inference for statistics, social, and biomedical sciences : an introduction, Guido W. Imbens & Donald B. Rubin represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in European University Institute.This item is available to borrow from 1 library branch.
Resource Information
The item Causal inference for statistics, social, and biomedical sciences : an introduction, Guido W. Imbens & Donald B. Rubin represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in European University Institute.
This item is available to borrow from 1 library branch.
 Summary
 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 worldrenowned 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, propensityscore methods, and instrumental variables. Many detailed applications are included, with special focus on practical aspects for the empirical researcher.
 Language
 eng
 Extent
 xix, 625 pages
 Contents

 Part 1: Introduction
 Causality: The Basic Framework
 A Brief History of the Potential Outcomes Approach to Causal Inference
 A Classification of Assignment Mechanisms
 Part 2: Classical Randomized Experiments
 A Taxonomy of Classical Randomized Experiments
 Fisher's Exact PValues for Completely Randomized Experiments
 Neyman's Repeated Sampling Approach to Completely Randomized Experiments
 Regression Methods for Completely Randomized Experiments
 ModelBased Inference for Completely Randomized Experiments
 Stratified Randomized Experiments
 Pairwise Randomized Experiments
 Case Study: An Experimental Evaluation of a Labor Market Program
 Part 3: Regular Assignment Mechanisms: Design
 Unconfounded Treatment Assignment
 Estimating the Propensity Score
 Assessing Overlap in Covariate Distributions
 Matching to Improve Balance in Covariate Distributions
 Trimming to Improve Balance in Covariate Distribution
 Part 4: Regular Assignment Mechanisms: Analysis
 Subclassification on the Propensity Score
 Matching Estimators
 A General Method for Estimating Sampling Variances for Standard Estimators for Average Causal Effects
 Inference for General Causal Estimands
 Part 5: Regular Assignment Mechanisms: Supplementary Analyses
 Assessing Unconfoundedness
 Sensitivity Analysis and Bounds
 Part 6: Regular Assignment Mechanisms with Noncompliance: Analysis
 Instrumental Variables Analysis of Randomized Experiments with OneSided Noncompliance
 Instrumental Variables Analysis of Randomized Experiments with TwoSided Noncompliance
 ModelBased Analysis in Instrumental Variable Settings: Randomized Experiments with TwoSided Noncompliance
 Part 7: Conclusion
 Conclusions and Extensions
 Isbn
 9780521885881
 Label
 Causal inference for statistics, social, and biomedical sciences : an introduction
 Title
 Causal inference for statistics, social, and biomedical sciences
 Title remainder
 an introduction
 Statement of responsibility
 Guido W. Imbens & Donald B. Rubin
 Language
 eng
 Summary
 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 worldrenowned 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, propensityscore methods, and instrumental variables. Many detailed applications are included, with special focus on practical aspects for the empirical researcher.
 Assigning source
 Provided by Publisher
 Cataloging source
 DLC
 http://library.link/vocab/creatorName
 Imbens, Guido
 Index
 no index present
 Literary form
 non fiction
 http://library.link/vocab/relatedWorkOrContributorName
 Rubin, Donald B
 http://library.link/vocab/subjectName

 Social sciences
 Causation
 Inference
 Label
 Causal inference for statistics, social, and biomedical sciences : an introduction, Guido W. Imbens & Donald B. Rubin
 Bibliography note
 Includes bibliographical references ( pages 591604) and indexes
 Carrier category
 volume
 Carrier category code

 nc
 Carrier MARC source
 rdacarrier.
 Content category
 text
 Content type code

 txt
 Content type MARC source
 rdacontent.
 Contents
 Part 1: Introduction  Causality: The Basic Framework  A Brief History of the Potential Outcomes Approach to Causal Inference  A Classification of Assignment Mechanisms  Part 2: Classical Randomized Experiments  A Taxonomy of Classical Randomized Experiments  Fisher's Exact PValues for Completely Randomized Experiments  Neyman's Repeated Sampling Approach to Completely Randomized Experiments  Regression Methods for Completely Randomized Experiments  ModelBased Inference for Completely Randomized Experiments  Stratified Randomized Experiments  Pairwise Randomized Experiments  Case Study: An Experimental Evaluation of a Labor Market Program  Part 3: Regular Assignment Mechanisms: Design  Unconfounded Treatment Assignment  Estimating the Propensity Score  Assessing Overlap in Covariate Distributions  Matching to Improve Balance in Covariate Distributions  Trimming to Improve Balance in Covariate Distribution  Part 4: Regular Assignment Mechanisms: Analysis  Subclassification on the Propensity Score  Matching Estimators  A General Method for Estimating Sampling Variances for Standard Estimators for Average Causal Effects  Inference for General Causal Estimands  Part 5: Regular Assignment Mechanisms: Supplementary Analyses  Assessing Unconfoundedness  Sensitivity Analysis and Bounds  Part 6: Regular Assignment Mechanisms with Noncompliance: Analysis  Instrumental Variables Analysis of Randomized Experiments with OneSided Noncompliance  Instrumental Variables Analysis of Randomized Experiments with TwoSided Noncompliance  ModelBased Analysis in Instrumental Variable Settings: Randomized Experiments with TwoSided Noncompliance  Part 7: Conclusion  Conclusions and Extensions
 Control code
 FIEb17699435
 Dimensions
 26 cm.
 Extent
 xix, 625 pages
 Isbn
 9780521885881
 Media category
 unmediated
 Media MARC source
 rdamedia.
 Media type code

 n
 System control number
 (OCoLC)933294021
 Label
 Causal inference for statistics, social, and biomedical sciences : an introduction, Guido W. Imbens & Donald B. Rubin
 Bibliography note
 Includes bibliographical references ( pages 591604) and indexes
 Carrier category
 volume
 Carrier category code

 nc
 Carrier MARC source
 rdacarrier.
 Content category
 text
 Content type code

 txt
 Content type MARC source
 rdacontent.
 Contents
 Part 1: Introduction  Causality: The Basic Framework  A Brief History of the Potential Outcomes Approach to Causal Inference  A Classification of Assignment Mechanisms  Part 2: Classical Randomized Experiments  A Taxonomy of Classical Randomized Experiments  Fisher's Exact PValues for Completely Randomized Experiments  Neyman's Repeated Sampling Approach to Completely Randomized Experiments  Regression Methods for Completely Randomized Experiments  ModelBased Inference for Completely Randomized Experiments  Stratified Randomized Experiments  Pairwise Randomized Experiments  Case Study: An Experimental Evaluation of a Labor Market Program  Part 3: Regular Assignment Mechanisms: Design  Unconfounded Treatment Assignment  Estimating the Propensity Score  Assessing Overlap in Covariate Distributions  Matching to Improve Balance in Covariate Distributions  Trimming to Improve Balance in Covariate Distribution  Part 4: Regular Assignment Mechanisms: Analysis  Subclassification on the Propensity Score  Matching Estimators  A General Method for Estimating Sampling Variances for Standard Estimators for Average Causal Effects  Inference for General Causal Estimands  Part 5: Regular Assignment Mechanisms: Supplementary Analyses  Assessing Unconfoundedness  Sensitivity Analysis and Bounds  Part 6: Regular Assignment Mechanisms with Noncompliance: Analysis  Instrumental Variables Analysis of Randomized Experiments with OneSided Noncompliance  Instrumental Variables Analysis of Randomized Experiments with TwoSided Noncompliance  ModelBased Analysis in Instrumental Variable Settings: Randomized Experiments with TwoSided Noncompliance  Part 7: Conclusion  Conclusions and Extensions
 Control code
 FIEb17699435
 Dimensions
 26 cm.
 Extent
 xix, 625 pages
 Isbn
 9780521885881
 Media category
 unmediated
 Media MARC source
 rdamedia.
 Media type code

 n
 System control number
 (OCoLC)933294021
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