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  • 1
    UID:
    almahu_9948233424402882
    Format: 1 online resource (xxiii, 499 pages) : , digital, PDF file(s).
    Edition: Second edition.
    ISBN: 9781107587991 (ebook)
    Series Statement: Analytical methods for social research
    Content: In this second edition of Counterfactuals and Causal Inference, completely revised and expanded, the essential features of the counterfactual approach to observational data analysis are presented with examples from the social, demographic, and health sciences. Alternative estimation techniques are first introduced using both the potential outcome model and causal graphs; after which, conditioning techniques, such as matching and regression, are presented from a potential outcomes perspective. For research scenarios in which important determinants of causal exposure are unobserved, alternative techniques, such as instrumental variable estimators, longitudinal methods, and estimation via causal mechanisms, are then presented. The importance of causal effect heterogeneity is stressed throughout the book, and the need for deep causal explanation via mechanisms is discussed.
    Note: Title from publisher's bibliographic system (viewed on 05 Oct 2015). , Machine generated contents note: Part I. Causality and Empirical Research in the Social Sciences: 1. Introduction; Part II. Counterfactuals, Potential Outcomes, and Causal Graphs: 2. Counterfactuals and the potential-outcome model; 3. Causal graphs; Part III. Estimating Causal Effects by Conditioning on Observed Variables to Block Backdoor Paths: 4. Models of causal exposure and identification criteria for conditioning estimators; 5. Matching estimators of causal effects; 6. Regression estimators of causal effects; 7. Weighted regression estimators of causal effects; Part IV. Estimating Causal Effects When Backdoor Conditioning is Ineffective: 8. Self-selection, heterogeneity, and causal graphs; 9. Instrumental-variable estimators of causal effects; 10. Mechanisms and causal explanation; 11. Repeated observations and the estimation of causal effects; Part V. Estimation When Causal Effects Are Not Point Identified by Observables: 12. Distributional assumptions, set identification, and sensitivity analysis; Part VI. Conclusions: 13. Counterfactuals and the future of empirical research in observational social science.
    Additional Edition: Print version: ISBN 9781107065079
    Language: English
    Subjects: Economics , Mathematics , Sociology
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    URL: Volltext  (URL des Erstveröffentlichers)
    URL: Volltext  (lizenzpflichtig)
    URL: Volltext  (URL des Erstveröffentlichers)
    Library Location Call Number Volume/Issue/Year Availability
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  • 2
    UID:
    almahu_BV042075300
    Format: xxiii, 499 Seiten : , Diagramme.
    Edition: Second edition
    ISBN: 978-1-107-06507-9 , 978-1-107-69416-3
    Series Statement: Analytical methods for social research
    Language: English
    Subjects: Economics , Sociology
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    Keywords: Sozialwissenschaften ; Kausalanalyse ; Kontrafaktisches Denken ; Sozialwissenschaften ; Methode
    URL: Cover
    Library Location Call Number Volume/Issue/Year Availability
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  • 3
    UID:
    almafu_BV043696950
    Format: xxiii, 499 Seiten : , Illustrationen, Diagramme.
    Edition: Second edition, reprinted with corrections
    ISBN: 978-1-107-06507-9 , 978-1-107-69416-3
    Series Statement: Analytical methods for social research
    Note: Hier auch später erschienene, unveränderte Nachdrucke
    Language: English
    Subjects: Economics , Mathematics , Sociology
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    Keywords: Sozialwissenschaften ; Kausalanalyse ; Kontrafaktisches Denken ; Sozialwissenschaften ; Methode
    Library Location Call Number Volume/Issue/Year Availability
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  • 4
    UID:
    kobvindex_GFZ796488819
    Format: XXIII, 499 S. , graph. Darst
    Edition: 2. ed., reprinted with corr., 3. print.
    ISBN: 9781107065079 (hardback) , 9781107694163 (paperback)
    Series Statement: Analytical methods for social research
    Content: In this second edition of Counterfactuals and Causal Inference, completely revised and expanded, the essential features of the counterfactual approach to observational data analysis are presented with examples from the social, demographic, and health sciences. Alternative estimation techniques are first introduced using both the potential outcome model and causal graphs; after which, conditioning techniques, such as matching and regression, are presented from a potential outcomes perspective. For reseach scenarios in which important determinants of causal exposure are unobserved, alternative techniques, such as instrumental variable estimators, longitudinal methods, and estimation via causal mechanisms, are then presented. The importance of causal effect heterogeneity is stressed throughout the book, and the need for deep causal explanation via mechanisms is discussed.
    Note: Part I. Causality and Empirical Research in the Social Sciences: 1. Introduction ; Part II. Counterfactuals, Potential Outcomes, and Causal Graphs: 2. Counterfactuals and the potential-outcome model ; 3. Causal graphs ; Part III. Estimating Causal Effects by Conditioning on Observed Variables to Block Backdoor Paths: 4. Models of causal exposure and identification criteria for conditioning estimators ; 5. Matching estimators of causal effects ; 6. Regression estimators of causal effects ; 7. Weighted regression estimators of causal effects ; Part IV. Estimating Causal Effects When Backdoor Conditioning Is Ineffective: 8. Self-selection, heterogeneity, and causal graphs ; 9. Instrumental-variable estimators of causal effects ; 10. Mechanisms and causal explanation ; 11. Repeated observations and the estimation of causal effects ; Part V. Estimation When Causal Effects Are Not Point Identified by Observables: 12. Distributional assumptions, set identification, and sensitivity analysis ; Part VI. Conclusions: 13. Counterfactuals and the future of empirical research in observational social science.
    Language: English
    Subjects: Sociology
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    Library Location Call Number Volume/Issue/Year Availability
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  • 5
    UID:
    edocfu_9960119329002883
    Format: 1 online resource (xxiii, 499 pages) : , digital, PDF file(s).
    Edition: Second edition.
    ISBN: 1-316-16398-9 , 1-316-16444-6 , 1-107-58799-9
    Series Statement: Analytical methods for social research
    Content: In this second edition of Counterfactuals and Causal Inference, completely revised and expanded, the essential features of the counterfactual approach to observational data analysis are presented with examples from the social, demographic, and health sciences. Alternative estimation techniques are first introduced using both the potential outcome model and causal graphs; after which, conditioning techniques, such as matching and regression, are presented from a potential outcomes perspective. For research scenarios in which important determinants of causal exposure are unobserved, alternative techniques, such as instrumental variable estimators, longitudinal methods, and estimation via causal mechanisms, are then presented. The importance of causal effect heterogeneity is stressed throughout the book, and the need for deep causal explanation via mechanisms is discussed.
    Note: Title from publisher's bibliographic system (viewed on 05 Oct 2015). , Machine generated contents note: Part I. Causality and Empirical Research in the Social Sciences: 1. Introduction; Part II. Counterfactuals, Potential Outcomes, and Causal Graphs: 2. Counterfactuals and the potential-outcome model; 3. Causal graphs; Part III. Estimating Causal Effects by Conditioning on Observed Variables to Block Backdoor Paths: 4. Models of causal exposure and identification criteria for conditioning estimators; 5. Matching estimators of causal effects; 6. Regression estimators of causal effects; 7. Weighted regression estimators of causal effects; Part IV. Estimating Causal Effects When Backdoor Conditioning is Ineffective: 8. Self-selection, heterogeneity, and causal graphs; 9. Instrumental-variable estimators of causal effects; 10. Mechanisms and causal explanation; 11. Repeated observations and the estimation of causal effects; Part V. Estimation When Causal Effects Are Not Point Identified by Observables: 12. Distributional assumptions, set identification, and sensitivity analysis; Part VI. Conclusions: 13. Counterfactuals and the future of empirical research in observational social science. , English
    Additional Edition: ISBN 1-107-69416-7
    Additional Edition: ISBN 1-107-06507-0
    Language: English
    Library Location Call Number Volume/Issue/Year Availability
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