Propensity score analysis: statistical methods and applications
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Los Angeles [u.a.]
SAGE
2010
|
| Schriftenreihe: | Advanced quantitative techniques in the social sciences series
12 |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Hier auch später erschienene, unveränderte Nachdrucke Includes bibliographical references and index |
| Beschreibung: | XVIII, 370 S. graph. Darst. |
| ISBN: | 9781412953566 |
Internformat
MARC
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| 008 | 101109s2010 xxud||| |||| 00||| eng d | ||
| 020 | |a 9781412953566 |c cloth |9 978-1-4129-5356-6 | ||
| 035 | |a (OCoLC)699520289 | ||
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| 100 | 1 | |a Guo, Shenyang |e Verfasser |0 (DE-588)170966216 |4 aut | |
| 245 | 1 | 0 | |a Propensity score analysis |b statistical methods and applications |c Shenyang Guo ; Mark W. Fraser |
| 264 | 1 | |a Los Angeles [u.a.] |b SAGE |c 2010 | |
| 300 | |a XVIII, 370 S. |b graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 1 | |a Advanced quantitative techniques in the social sciences series |v 12 | |
| 500 | |a Hier auch später erschienene, unveränderte Nachdrucke | ||
| 500 | |a Includes bibliographical references and index | ||
| 650 | 4 | |a Sozialwissenschaften | |
| 650 | 4 | |a Social sciences |x Statistical methods | |
| 650 | 4 | |a Analysis of variance | |
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| 689 | 0 | |5 DE-188 | |
| 700 | 1 | |a Fraser, Mark W. |d 1946- |e Verfasser |0 (DE-588)1017333289 |4 aut | |
| 830 | 0 | |a Advanced quantitative techniques in the social sciences series |v 12 |w (DE-604)BV023546702 |9 12 | |
| 856 | 4 | 2 | |m Digitalisierung UB Bayreuth |q application/pdf |u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=020681719&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |3 Inhaltsverzeichnis |
| 999 | |a oai:aleph.bib-bvb.de:BVB01-020681719 | ||
Datensatz im Suchindex
| _version_ | 1804143433543057408 |
|---|---|
| adam_text | Contents
List of Tables
ix
List of Figures
xv
Acknowledgments
xvii
1.
Introduction
1
1.1
Observational Studies
3
1.2
History and Development
4
1.3
Randomized Experiments
5
1.3.1
Fisher s Randomized Experiment
6
1.3.2
Types of Randomized Experiments and Statistical Tests
10
1.3.3
Critiques of Social Experimentation
11
1.4
Why and When a Propensity Score Analysis Is Needed
12
1.5
Computing Software Packages
17
1.6
Plan of the Book
17
2.
Counterfactual Framework and Assumptions
21
2.
і
Causality, Internal Validity, and Threats
21
2.2
Counterfactuals and the Neyman-Rubin Counterfactual
Framework
24
2.3
The
Ignorable
Treatment Assignment Assumption
30
2.4
The Stable Unit Treatment Value Assumption
35
2.5
Methods to Estimate Treatment Effects
36
2.5.1
The Four Models
36
2.5.2
Other Bafondng Methods
39
2.6
The Underlying Logic of Statistical Inference
39
2.7
Types of Treatment Effects
46
2.8
Heckman s Econometric Model of Causality
50
2.9
Conclusions
53
3.
Conventional Methods for Data Balancing
55
3.1
Why Is Data Balancing Necessary? A Heuristic Example
56
3.2
Three Methods of Data Balancing
61
3.2.1
The Ordinary Least Squares Regression
61
3.2.2
Matching
64
3.2.3
Stratification
65
3.3
Design of the Data Simulation
67
3.4
Results of the Data Simulation
69
3.5
Implications of the Data Simulation
79
3.6
Key Issues Regarding the Application of OLS Regression
82
3.7
Conclusions
84
4.
Sample Selection and Related Models
85
4.1
The Sample Selection Model
86
4.1.1
Truncation, Censoring, and Incidental Truncation
86
4.1.2
Why Is It Important to Model Sample Selection?
90
4.1.3
Moments of an Incidentally Truncated Bivariate
Normal Distribution
91
4.1.4
The
Heekman
Model and Its Two-Step Estimator
92
4.2
Treatment Effect Model
96
4.3
Instrumental Variables Estimator
98
4.4
Overview of the
Stata
Programs and
Main Features of treatreg
100
4.5
Examples
106
4.5.1
Application of the Treatment Effect Model
to Analysis of Observational Data
108
4.5.2
Evaluation of Treatment Effects From a Program
With a Group Randomization Design
113
4.5.3
Running the Treatment Effect Model After Multiple
Imputarìons
of Missing Data
121
4.6
Conclusions
124
5.
Propensity Score Matching and Related Models
127
5.1
Overview
128
5.2
The Problem of Dimensionality and the Properties
of Propensity Scores
132
5.3
Estimating Propensity Scores
135
5.3.1
Binary Logistic Regression
135
5.3.2
Strategies to Specify a Correct Model
Predicting Propensity Scores
138
5.3.3
Hirano and Imbens s Method for Specifying
Predictors Relying on Predetermined
Criticai
t
Values
140
5.3.4
Generalized Boosted Modeling
143
5.4
Matching
144
5.4.1
Greedy Matching
145
5.4.2
Optimal Matching
149
5.4.3
Fine Balance
153
5.5
Postmatching Analysis
154
5.5.1
Multivariate Analysis After Greedy Matching
154
5.5.2
Stratification After Greedy Matching
155
5.5.3 Computing
índices
of Covariate Imbalance
157
5.5.4
Outcome Analysis Using the Hodges-Lehmann Aligned
Rank Test After Optimal Matching
158
5.5.5
Regression Adjustment Based on Sample Created by
Optimal Pair Matching
159
5.5.6
Regression Adjustment Using Hodges-Lehmann
Aligned Rank Scores After Optimal Matching
160
5.6
Propensity Score Weighting
161
5.7
Modeling Doses of Treatment
162
5.8
Overview of the
Stata
and
R
Programs
167
5.9
Examples
174
5.9.1
Greedy Matching and Subsequent Analysis of
Hazard Rates
175
5.9.2
Optimal Matching
187
5.9.3
Post-Full Matching Analysis Using the
Hodges-Lehmann Aligned Rank Test
195
5.9.4
Post-Pair Matching Analysis Using Regression of
Difference Scores
195
5.9.5
Propensity Score Weighting
197
5.9.6
Modeling Doses of Treatment
199
5.9.7
Comparison of Models and Conclusions of the
Study of the Impact of Poverty on Child Academic
Achievement
204
5.9.8
Comparison ofRand-gbm and Status Boost Algorithms
206
5.10
Conclusions
208
6.
Matching Estimators
211
6.1
Overview
212
6.2
Methods of Matching Estimators
216
6.2.1
Simple Matching Estimator
216
6.2.2
Bias-Corrected Matching Estimator
224
6.2.3
Variance Estimator Assuming Homoscedastkity
225
6.2.4
Variance Estimator Allowing for Heteroscedasticity
227
6.2.5
Large Sample Properties and Correction
228
6.3
Overview of the
Stata
Program nnmatch
228
6.4
Examples
230
6.4.1
Matching With Bias-Corrected and Robust
Variance Estimators
230
6.4.2
Efficacy Subset Analysis With Matching Estimators
236
6.5
Conclusions
243
7.
Propensity Score Analysis With Nonparametric Regression
245
7.1
Overview
246
7.2
Methods of Propensity Score Analysis With Nonparametric
Regression
249
7.2.1
The Kernel-Based Matching Estimators
249
7.2.2 Review
of the Basic Concepts of Local Linear
Regression (lowess)
251
7.2.3
Asymptotic and Finite-Sample Properties of
Kernel and Local Linear Matching
260
7.3
Overview of the
Stata
Programs
pstnatàû.
and bootstrap
261
7.4
Examples
267
7.4.1
AnalysL· of Difference-in-Differences
268
7.4.2
Application of Kernel-Based Matching to
One-Point Data
271
7.5
Conclusions
273
8.
Selection Bias and Sensitivity Analysis
275
8.1
Selection Bias: An Overview
275
8.1.1
Sources of Selection Bias
276
8.1.2
Overt Bias Versus Hidden Bias
280
8.1.3
Consequences of Selection Bias
281
8.1.4
Strategies to Correct for Selection Bias
282
8.2
A Monte Carlo Study Comparing Corrective Models
285
8.2.1
Design of the Monte Carlo Study
288
8.2.2
Results of the Monte Carlo Study
293
8.2.3
Implications
296
8.3
Rosenbaum s Sensitivity Analysis
297
8.3.1
The Basic Idea
297
8.3.2
Illustration of the Wkoxon s Signed-Rank Test
for Sensitivity Analysis of Matched Pair Study
299
8.4
Overview of the
Stata
Program rbounds
309
8.5
Examples
316
8.5.1
Sensitivity Analysis of the Effects of Lead Exposure
316
8.5.2
Sensitivity
Analysu
for the Study Using Pair Matching
317
8.6
Conclusions
319
9.
Concluding Remarks
321
9.1
Common Pitfalls in Observational Studies:
A Checklist for Critical Review
321
9.2
Approximating Experiments With Propensity
Score Approaches
326
9.2.1
Criticism of Propensity Score Methods
327
9.2.2
Critidsm of Sensitivity Analysis
(Г)
328
9.2.3
Group Randomized
Triais
328
93
Other Advances in Modeling Causality
330
9.4
Directions for Future Development
331
References
335
Author Index
347
Subject Index
353
About the Authors
369
|
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| id | DE-604.BV036764754 |
| illustrated | Illustrated |
| indexdate | 2024-07-09T22:47:35Z |
| institution | BVB |
| isbn | 9781412953566 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-020681719 |
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| physical | XVIII, 370 S. graph. Darst. |
| publishDate | 2010 |
| publishDateSearch | 2010 |
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| publisher | SAGE |
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| series | Advanced quantitative techniques in the social sciences series |
| series2 | Advanced quantitative techniques in the social sciences series |
| spelling | Guo, Shenyang Verfasser (DE-588)170966216 aut Propensity score analysis statistical methods and applications Shenyang Guo ; Mark W. Fraser Los Angeles [u.a.] SAGE 2010 XVIII, 370 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Advanced quantitative techniques in the social sciences series 12 Hier auch später erschienene, unveränderte Nachdrucke Includes bibliographical references and index Sozialwissenschaften Social sciences Statistical methods Analysis of variance Anwendung (DE-588)4196864-5 gnd rswk-swf Statistik (DE-588)4056995-0 gnd rswk-swf Statistik (DE-588)4056995-0 s Anwendung (DE-588)4196864-5 s DE-188 Fraser, Mark W. 1946- Verfasser (DE-588)1017333289 aut Advanced quantitative techniques in the social sciences series 12 (DE-604)BV023546702 12 Digitalisierung UB Bayreuth application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=020681719&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Guo, Shenyang Fraser, Mark W. 1946- Propensity score analysis statistical methods and applications Advanced quantitative techniques in the social sciences series Sozialwissenschaften Social sciences Statistical methods Analysis of variance Anwendung (DE-588)4196864-5 gnd Statistik (DE-588)4056995-0 gnd |
| subject_GND | (DE-588)4196864-5 (DE-588)4056995-0 |
| title | Propensity score analysis statistical methods and applications |
| title_auth | Propensity score analysis statistical methods and applications |
| title_exact_search | Propensity score analysis statistical methods and applications |
| title_full | Propensity score analysis statistical methods and applications Shenyang Guo ; Mark W. Fraser |
| title_fullStr | Propensity score analysis statistical methods and applications Shenyang Guo ; Mark W. Fraser |
| title_full_unstemmed | Propensity score analysis statistical methods and applications Shenyang Guo ; Mark W. Fraser |
| title_short | Propensity score analysis |
| title_sort | propensity score analysis statistical methods and applications |
| title_sub | statistical methods and applications |
| topic | Sozialwissenschaften Social sciences Statistical methods Analysis of variance Anwendung (DE-588)4196864-5 gnd Statistik (DE-588)4056995-0 gnd |
| topic_facet | Sozialwissenschaften Social sciences Statistical methods Analysis of variance Anwendung Statistik |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=020681719&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV023546702 |
| work_keys_str_mv | AT guoshenyang propensityscoreanalysisstatisticalmethodsandapplications AT frasermarkw propensityscoreanalysisstatisticalmethodsandapplications |