Counterfactuals and causal inference: methods and principles for social research
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 f...
Gespeichert in:
| 1. Verfasser: | |
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Cambridge
Cambridge University Press
2015
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| Ausgabe: | Second edition |
| Schriftenreihe: | Analytical methods for social research
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| Schlagworte: | |
| Online-Zugang: | DE-20 Volltext |
| Zusammenfassung: | 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 |
| Beschreibung: | Title from publisher's bibliographic system (viewed on 05 Oct 2015) |
| Beschreibung: | 1 Online-Ressource (xxiii, 499 Seiten) |
| ISBN: | 9781107587991 |
| DOI: | 10.1017/CBO9781107587991 |
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| 246 | 1 | 3 | |a Counterfactuals & Causal Inference |
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| 505 | 8 | |a 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 | |
| 520 | |a 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 | ||
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| author | Morgan, Stephen L. 1971- |
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| contents | 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 |
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| dewey-full | 300.72 |
| dewey-hundreds | 300 - Social sciences |
| dewey-ones | 300 - Social sciences |
| dewey-raw | 300.72 |
| dewey-search | 300.72 |
| dewey-sort | 3300.72 |
| dewey-tens | 300 - Social sciences |
| discipline | Soziologie Mathematik Wirtschaftswissenschaften |
| discipline_str_mv | Soziologie |
| doi_str_mv | 10.1017/CBO9781107587991 |
| edition | Second edition |
| format | Electronic eBook |
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| id | DE-604.BV049636685 |
| illustrated | Not Illustrated |
| index_date | 2024-07-03T23:39:02Z |
| indexdate | 2024-07-20T07:27:14Z |
| institution | BVB |
| isbn | 9781107587991 |
| language | English |
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| physical | 1 Online-Ressource (xxiii, 499 Seiten) |
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| publishDate | 2015 |
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| series2 | Analytical methods for social research |
| spelling | Morgan, Stephen L. 1971- Verfasser aut Counterfactuals and causal inference methods and principles for social research Stephen L. Morgan, Christopher Winship Counterfactuals & Causal Inference Second edition Cambridge Cambridge University Press 2015 1 Online-Ressource (xxiii, 499 Seiten) txt rdacontent c rdamedia cr rdacarrier Analytical methods for social research 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 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 Social sciences / Research Social sciences / Methodology Causation Methode (DE-588)4038971-6 gnd rswk-swf Sozialwissenschaften (DE-588)4055916-6 gnd rswk-swf Kausalanalyse (DE-588)4163511-5 gnd rswk-swf Kontrafaktisches Denken (DE-588)4577003-7 gnd rswk-swf Sozialwissenschaften (DE-588)4055916-6 s Kausalanalyse (DE-588)4163511-5 s Kontrafaktisches Denken (DE-588)4577003-7 s DE-604 Methode (DE-588)4038971-6 s Winship, Christopher Sonstige oth Erscheint auch als Druck-Ausgabe 9781107065079 Erscheint auch als Druck-Ausgabe 9781107694163 https://doi.org/10.1017/CBO9781107587991 Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Morgan, Stephen L. 1971- Counterfactuals and causal inference methods and principles for social research 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 Social sciences / Research Social sciences / Methodology Causation Methode (DE-588)4038971-6 gnd Sozialwissenschaften (DE-588)4055916-6 gnd Kausalanalyse (DE-588)4163511-5 gnd Kontrafaktisches Denken (DE-588)4577003-7 gnd |
| subject_GND | (DE-588)4038971-6 (DE-588)4055916-6 (DE-588)4163511-5 (DE-588)4577003-7 |
| title | Counterfactuals and causal inference methods and principles for social research |
| title_alt | Counterfactuals & Causal Inference |
| title_auth | Counterfactuals and causal inference methods and principles for social research |
| title_exact_search | Counterfactuals and causal inference methods and principles for social research |
| title_exact_search_txtP | Counterfactuals and causal inference methods and principles for social research |
| title_full | Counterfactuals and causal inference methods and principles for social research Stephen L. Morgan, Christopher Winship |
| title_fullStr | Counterfactuals and causal inference methods and principles for social research Stephen L. Morgan, Christopher Winship |
| title_full_unstemmed | Counterfactuals and causal inference methods and principles for social research Stephen L. Morgan, Christopher Winship |
| title_short | Counterfactuals and causal inference |
| title_sort | counterfactuals and causal inference methods and principles for social research |
| title_sub | methods and principles for social research |
| topic | Social sciences / Research Social sciences / Methodology Causation Methode (DE-588)4038971-6 gnd Sozialwissenschaften (DE-588)4055916-6 gnd Kausalanalyse (DE-588)4163511-5 gnd Kontrafaktisches Denken (DE-588)4577003-7 gnd |
| topic_facet | Social sciences / Research Social sciences / Methodology Causation Methode Sozialwissenschaften Kausalanalyse Kontrafaktisches Denken |
| url | https://doi.org/10.1017/CBO9781107587991 |
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