Counterfactuals and causal inference: methods and principles for social research
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Cambridge [u.a.]
Cambridge Univ. Press
2008
|
| Schriftenreihe: | Analytical methods for social research
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Hier auch später erschienene, unveränd. Nachdrucke Literaturverz. S. 291 - 316 ; Index |
| Beschreibung: | XIII, 319 S. graph. Darst. |
| ISBN: | 9780521856157 9780521671934 |
Internformat
MARC
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| 100 | 1 | |a Morgan, Stephen L. |d 1971- |e Verfasser |0 (DE-588)133718859 |4 aut | |
| 245 | 1 | 0 | |a Counterfactuals and causal inference |b methods and principles for social research |c Stephen L. Morgan ; Christopher Winship |
| 264 | 1 | |a Cambridge [u.a.] |b Cambridge Univ. Press |c 2008 | |
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| 490 | 0 | |a Analytical methods for social research | |
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| 650 | 4 | |a Sozialwissenschaften | |
| 650 | 4 | |a Social sciences |x Research | |
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Datensatz im Suchindex
| _version_ | 1804138316432408576 |
|---|---|
| adam_text | Contents
Part
1 : Counterfactual
Causality and Empirical Research in the
Social Sciences page
1
1
Introduction
3
1.1
The Counterfactual Model for Observational Data Analysis
4
1.2
Causal Analysis and Observational Social Science
6
1.3
Types of Examples Used Throughout the Book
13
1.4
Observational Data and Random-Sample Surveys
21
1.5
Identification and Statistical Inference
22
1.6
Causal Graphs as an Introduction to the Remainder
of the Book
24
2
The Counterfactual Model
31
2.1
Causal States and Potential Outcomes
31
2.2
Treatment Groups and Observed Outcomes
34
2.3
The Average Treatment Effect
35
2.4
The Stable Unit Treatment Value Assumption
37
2.5
Treatment Assignment and Observational Studies
40
2.6
Average Causal Effects and Naive Estimation
42
2.7
Conclusions
50
Appendix A: Population and Data Generation Models
51
Appendix B: Extension of the Framework to
Many-Valued Treatments
53
Part
2:
Estimating Causal Effects by Conditioning
59
3
Causal Graphs, Identification, and Models of Causal Exposure
61
3.1
Causal Graphs and Conditioning as Back-Door
Identification
61
3.2
Models of Causal Exposure in the Counterfactual Tradition
74
3.3
Conditioning to Balance versus Conditioning to Adjust
81
χ
Contents
3.4 Point Identification
of Conditional Average Treatment
Effects by Conditioning
83
3.5
Conclusions
85
4
Matching Estimators of Causal Effects
87
4.1
Origins of and Motivations for Matching
88
4.2
Matching as Conditioning via Stratification
90
4.3
Matching as Weighting
98
4.4
Matching as a Data Analysis Algorithm
105
4.5
Matching When Treatment Assignment is Nonignorable
116
4.6
Remaining Practical Issues in Matching Analysis
116
4.7
Conclusions
121
5
Regression Estimators of Causal Effects
123
5.1
Regression as a Descriptive Tool
123
5.2
Regression Adjustment as a Strategy to Estimate
Causal Effects
129
5.3
The Connections Between Regression and Matching
142
5.4
Extensions and Other Perspectives
158
5.5
Conclusions
165
Part
3:
Estimating Causal Effects When Simple Conditioning
Is Ineffective
167
6
Identification in the Absence of a Complete Model of
Causal Exposure
169
6.1
Nonignorability and Selection on the Unobservables
Revisited
169
6.2
Sensitivity Analysis for Provisional Causal Effect Estimates
170
6.3
Partial Identification with Minimal Assumptions
172
6.4
Additional Strategies for the Point Identification of
Causal Effects
179
6.5
Conclusions
184
Appendix: Latent Variable Selection-Bias Models
184
7
Instrumental Variable Estimators of Causal Effects
187
7.1
Causal Effect Estimation with a Binary IV
187
7.2
Traditional IV Estimators
193
7.3
Recognized Pitfalls of Traditional IV Estimation
197
7.4
Instrumental Variable Estimators of Average Causal Effects
200
7.5
Two Additional Perspectives on the
Identification of Causal Effects with I Vs
213
7.6
Conclusions
216
8
Mechanisms and Causal Explanation
219
8.1
The Dangers of Insufficiently Deep Explanations
220
Contents xi
8.2
Explanation and Identification of Causal Effects
by Mechanisms
224
8.3
The Appeal for Generative Mechanisms
230
8.4
The Pursuit of Explanation by Mechanisms that
Bottom Out
238
8.5
Conclusion
242
9
Repeated Observations and the Estimation of Causal Effects
243
9.1
Interrupted Time Series Models
244
9.2
Regression Discontinuity Designs
250
9.3
Panel Data
253
9.4
Conclusions
274
Part
4:
Conclusions
275
10
Counterfactual Causality and Future Empirical Research in the
Social Sciences
277
10.1
Objections to Features of the Counterfactual Model
278
10.2
Modes of Causal Inquiry in the Social Sciences
285
References
291
Index
317
|
| adam_txt |
Contents
Part
1 : Counterfactual
Causality and Empirical Research in the
Social Sciences page
1
1
Introduction
3
1.1
The Counterfactual Model for Observational Data Analysis
4
1.2
Causal Analysis and Observational Social Science
6
1.3
Types of Examples Used Throughout the Book
13
1.4
Observational Data and Random-Sample Surveys
21
1.5
Identification and Statistical Inference
22
1.6
Causal Graphs as an Introduction to the Remainder
of the Book
24
2
The Counterfactual Model
31
2.1
Causal States and Potential Outcomes
31
2.2
Treatment Groups and Observed Outcomes
34
2.3
The Average Treatment Effect
35
2.4
The Stable Unit Treatment Value Assumption
37
2.5
Treatment Assignment and Observational Studies
40
2.6
Average Causal Effects and Naive Estimation
42
2.7
Conclusions
50
Appendix A: Population and Data Generation Models
51
Appendix B: Extension of the Framework to
Many-Valued Treatments
53
Part
2:
Estimating Causal Effects by Conditioning
59
3
Causal Graphs, Identification, and Models of Causal Exposure
61
3.1
Causal Graphs and Conditioning as Back-Door
Identification
61
3.2
Models of Causal Exposure in the Counterfactual Tradition
74
3.3
Conditioning to Balance versus Conditioning to Adjust
81
χ
Contents
3.4 Point Identification
of Conditional Average Treatment
Effects by Conditioning
83
3.5
Conclusions
85
4
Matching Estimators of Causal Effects
87
4.1
Origins of and Motivations for Matching
88
4.2
Matching as Conditioning via Stratification
90
4.3
Matching as Weighting
98
4.4
Matching as a Data Analysis Algorithm
105
4.5
Matching When Treatment Assignment is Nonignorable
116
4.6
Remaining Practical Issues in Matching Analysis
116
4.7
Conclusions
121
5
Regression Estimators of Causal Effects
123
5.1
Regression as a Descriptive Tool
123
5.2
Regression Adjustment as a Strategy to Estimate
Causal Effects
129
5.3
The Connections Between Regression and Matching
142
5.4
Extensions and Other Perspectives
158
5.5
Conclusions
165
Part
3:
Estimating Causal Effects When Simple Conditioning
Is Ineffective
167
6
Identification in the Absence of a Complete Model of
Causal Exposure
169
6.1
Nonignorability and Selection on the Unobservables
Revisited
169
6.2
Sensitivity Analysis for Provisional Causal Effect Estimates
170
6.3
Partial Identification with Minimal Assumptions
172
6.4
Additional Strategies for the Point Identification of
Causal Effects
179
6.5
Conclusions
184
Appendix: Latent Variable Selection-Bias Models
184
7
Instrumental Variable Estimators of Causal Effects
187
7.1
Causal Effect Estimation with a Binary IV
187
7.2
Traditional IV Estimators
193
7.3
Recognized Pitfalls of Traditional IV Estimation
197
7.4
Instrumental Variable Estimators of Average Causal Effects
200
7.5
Two Additional Perspectives on the
Identification of Causal Effects with I Vs
213
7.6
Conclusions
216
8
Mechanisms and Causal Explanation
219
8.1
The Dangers of Insufficiently Deep Explanations
220
Contents xi
8.2
Explanation and Identification of Causal Effects
by Mechanisms
224
8.3
The Appeal for Generative Mechanisms
230
8.4
The Pursuit of Explanation by Mechanisms that
Bottom Out
238
8.5
Conclusion
242
9
Repeated Observations and the Estimation of Causal Effects
243
9.1
Interrupted Time Series Models
244
9.2
Regression Discontinuity Designs
250
9.3
Panel Data
253
9.4
Conclusions
274
Part
4:
Conclusions
275
10
Counterfactual Causality and Future Empirical Research in the
Social Sciences
277
10.1
Objections to Features of the Counterfactual Model
278
10.2
Modes of Causal Inquiry in the Social Sciences
285
References
291
Index
317 |
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| id | DE-604.BV035155838 |
| illustrated | Illustrated |
| index_date | 2024-07-02T22:48:38Z |
| indexdate | 2024-07-09T21:26:15Z |
| institution | BVB |
| isbn | 9780521856157 9780521671934 |
| language | English |
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| spelling | Morgan, Stephen L. 1971- Verfasser (DE-588)133718859 aut Counterfactuals and causal inference methods and principles for social research Stephen L. Morgan ; Christopher Winship Cambridge [u.a.] Cambridge Univ. Press 2008 XIII, 319 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Analytical methods for social research Hier auch später erschienene, unveränd. Nachdrucke Literaturverz. S. 291 - 316 ; Index Sozialwissenschaften Social sciences Research Social sciences Methodology Causation Sozialwissenschaften (DE-588)4055916-6 gnd rswk-swf Methode (DE-588)4038971-6 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 Methode (DE-588)4038971-6 s DE-188 Kausalanalyse (DE-588)4163511-5 s Kontrafaktisches Denken (DE-588)4577003-7 s 1\p DE-604 Winship, Christopher Verfasser aut Digitalisierung UB Bamberg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016963025&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Morgan, Stephen L. 1971- Winship, Christopher Counterfactuals and causal inference methods and principles for social research Sozialwissenschaften Social sciences Research Social sciences Methodology Causation Sozialwissenschaften (DE-588)4055916-6 gnd Methode (DE-588)4038971-6 gnd Kausalanalyse (DE-588)4163511-5 gnd Kontrafaktisches Denken (DE-588)4577003-7 gnd |
| subject_GND | (DE-588)4055916-6 (DE-588)4038971-6 (DE-588)4163511-5 (DE-588)4577003-7 |
| title | Counterfactuals and causal inference methods and principles for social research |
| 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 | Sozialwissenschaften Social sciences Research Social sciences Methodology Causation Sozialwissenschaften (DE-588)4055916-6 gnd Methode (DE-588)4038971-6 gnd Kausalanalyse (DE-588)4163511-5 gnd Kontrafaktisches Denken (DE-588)4577003-7 gnd |
| topic_facet | Sozialwissenschaften Social sciences Research Social sciences Methodology Causation Methode Kausalanalyse Kontrafaktisches Denken |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016963025&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT morganstephenl counterfactualsandcausalinferencemethodsandprinciplesforsocialresearch AT winshipchristopher counterfactualsandcausalinferencemethodsandprinciplesforsocialresearch |