Propensity score analysis: statistical methods and applications
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Los Angeles [u.a.]
SAGE
2015
|
| Edition: | 2. ed. |
| Series: | Advanced quantitative techniques in the social sciences series
[12] |
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Item Description: | Fälschlich als Bd. 11 der Schriftenreihe bezeichnet Includes bibliographical references and index |
| Physical Description: | XXIV, 421 S. graph. Darst. |
| ISBN: | 9781452235004 |
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MARC
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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 |
| 250 | |a 2. ed. | ||
| 264 | 1 | |a Los Angeles [u.a.] |b SAGE |c 2015 | |
| 300 | |a XXIV, 421 S. |b graph. Darst. | ||
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| 490 | 1 | |a Advanced quantitative techniques in the social sciences series |v [12] | |
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| 500 | |a Includes bibliographical references and index | ||
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Record in the Search Index
| _version_ | 1804152109064519680 |
|---|---|
| adam_text | Brief Contents
List of Tables
xiii
List of Figures
xvii
Preface
xix
About the Authors
xxiii
1.
Introduction
1
2.
Counterfactual Framework and Assumptions
21
3.
Conventional Methods for Data Balancing
67
4.
Sample Selection and Related Models
95
5.
Propensity Score Matching and Related Models
129
6.
Propensity Score Subclassification
203
7.
Propensity Score Weighting
239
8.
Matching Estimators
255
9.
Propensity Score Analysis With Nonparametnc Regression
283
10.
Propensity Score Analysis of Categorical
or Continuous Treatments: Dosage Analyses
309
11.
Selection Bias and Sensitivity Analysis
335
12.
Concluding Remarks
381
References
395
Index
409
Detailed Contents
List of Tables xjji
List of Figures xvjj
Preface X1X
1.
What the Book Is About
xix
2.
New in the Second Edition
xx
3.
Acknowledgments
xxii
About the Authors
xxiii
± Introduction
1
1.1
Observational Studies
3
1.2
History and Development
4
1.3
Randomized Experiments
6
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
11
1.5
Computing Software Packages
16
1.6
Plan of the Book
17
Ĺ Counterfactual
Framework and Assumptions
21
2.1
Causality, Internal Validity, and Threats
22
2.2
Counterfactuals and the Neyman-Rubin Counterfactual Framework
23
2.3
The
Ignorable
Treatment Assignment Assumption
29
2.4
The Stable Unit Treatment Value Assumption
33
2.5
Methods for Estimating Treatment Effects
34
2.5.1
Design of Observational Study
3-4
2.5.2
The Seven Models
35
2.5.3
Other Balancing Methods
38
2.5.4
Instrumental Variables Estimator
38
2.5.5
Regression Discontinuity Designs
42
2.6
The Underlying Logic of Statistical Inference
43
2.7
Types of Treatment Effects
48
2.8
Treatment
Effect Heterogeneity
53
2.8.1
The Importance of Studying Treatment Effect Heterogeneity
53
2.8.2
Checking the Plausibility of the Unconfoundedness Assumption
55
2.8.3
A Methodological Note About the Hausman Test of Endogeneity
56
2.8.4
Tests of Treatment Effect Heterogeneity
57
2.8.5
Example
59
2.9
Heckman s Econometric Model of Causality
62
2.10
Conclusion
65
)
Conventional Methods for Data Balancing
67
3.1
Why Is Data Balancing Necessary? A Heuristic Example
68
3.2
Three Methods for Data Balancing
71
3.2.1
The Ordinary Least Squares Regression
71
3.2.2
Matching
75
3.2.3
Stratification
76
3.3
Design of the Data Simulation
77
3.4
Results of the Data Simulation
79
3.5
Implications of the Data Simulation
86
3.6
Key Issues Regarding the Application of OLS Regression
93
3.7
Conclusion
94
l· Sample Selection and Related Models
95
4.1
The Sample Selection Model
95
4.1.1
Truncation, Censoring, and Incidental Truncation
96
4.1.2
Why Is It Important to Model Sample Selection?
99
4.1.3
Moments of an Incidentally Truncated
Divariate
Normal Distribution
100
4.1.4
The
Heekman
Model and Its Two-Step Estimator
101
4.2
Treatment Effect Model
105
4.3
Overview of the
Stata
Programs and Main Features of treatreg
107
4.4
Examples
112
4.4.1
Application of the Treatment Effect Model to Analysis of Observational Data
112
4.4.2
Evaluation of Treatment Effects From a Program
With a Group Randomization Design
118
4.4.3
Running the Treatment Effect Model After Multiple Imputations of
Missino Data
125
4.5
Conclusion
127
>
Propensity Score Matching and Related Models 12q
5.1
Overview
130
5.2
The Problem of Dimensionality and the Properties of Propensity Scores
134
5.3
Estimating Propensity Scores
137
5.3.1
Binary Logistic Regression
137
5.3.2
Strategies to Specify a Correct Model-Predicting Propensity Scores
un
5.3.3
Hirano and Imbens s Method for Specifying Predictors
Relying on Predetermined Critical
t
Values
142
5.3.4
Generalized Boosted Modeling
144
5.4
Matching
145
7
5.4.1
Greedy Matching
145
5.4.2
Optimal Matching
148
5.4.3
Fine Balance
152
5.5
Postmatching Analysis
153
5.5.1
Multivariate Analysis After Greedy Matching
153
5.5.2
Computing Indices of Covariate Imbalance
154
5.5.3
Outcome Analysis Using the Hodges-Lehmann
ALigned Rank Test After Optimal Matching
155
5.5.4
Regression Adjustment Based on Sample Created by Optimal Pair Matching
156
5.5.5
Regression Adjustment Using Hodges-Lehmann
Aligned Rank Scores After Optimal Matching
157
5.6
Propensity Score Matching With Multilevel Data
157
5.6.1
Overview of Statistical Approaches to Multilevel Data
158
5.6.2
Perspectives Extending the Propensity Score Analysis to the Multilevel Modeling
160
5.6.3
Estimation of the Propensity Scores Under the Context of Multilevel Modeling
161
5.6.4
Multilevel Outcome Analysis
164
5.7
Overview of the
Stata
and
R
Programs
166
5.8
Examples
172
5.8.1
Greedy Matching and Subsequent Analysis of Hazard Rates
173
5.8.2
Optimal Matching
183
5.8.3
Post-Full Matching Analysis Using the Hodges-Lehmann Aligned Rank Test
190
5.8.4
Post-Pair Matching Analysis Using Regression of Difference Scores
191
5.8.5
Multilevel Propensity Score Analysis
191
5.8.6
Comparison of Rand-gbm and Stata s boost Algorithms
198
5.9
Conclusion
201
I Propensity Score Subclassification
203
6.1
Overview
204
6.2
The Overlap Assumption and Methods to Address Its Violation
209
6.3
Structural Equation Modeling With Propensity Score
Subclassification
210
6.3.1
The Need for Integrating
SEM
and Propensity Score Modeling Into One Analysis
210
6.3.2
Kaplan s
(1999)
Work to Integrate Propensity Score
Subclassification
With
SEM
216
6.3.3
Conduct
SEM
With Propensity Score
Subclassification
216
6.4
The Stratification-Multilevel Method
217
6.5
Examples
219
6.5.1
Stratification After Greedy Matching
219
6.5.2
Subclassification
Followed by a Cox Proportional Hazards Model
221
6.5.3
Propensity Score
Subclassification
in Conjunction With
SEM
234
6.6
Conclusion
236
Propensity Score Weighting
239
7.1
Overview
240
7.2
Weighting Estimators
244
7.2.1
Formulas for Creating Weights to Estimate ATE and ATT
244
7.2.2
A Corrected Version of Weights Estimating ATE
245
7.2.3
Steps in Propensity Score Weighting
246
7.3
Examples
246
7.3.1
Propensity Score Weighting With a Multiple Regression Outcome Analysis
246
7.3.2
Propensity Score Weighting With a Cox Proportional Hazards Model
247
7.3.3
Propensity Score Weighting With an
SEM
249
7.3.4
Comparison of Models and Conclusions of the Study of
the Impact of Poverty on Child Academic Achievement
252
7.4
Conclusion
254
О
Matching Estimators
255
8.1
Overview
255
8.2
Methods of Matching Estimators
259
8.2.1
Simple Matching Estimator
260
8.2.2
Bias-Corrected Matching Estimator
266
8.2.3
Variance Estimator Assuming Homoscedasticity
268
8.2.4
Variance Estimator Allowing for Heteroscedasticity
269
8.2.5
Large Sample Properties and Correction
269
8.3
Overview of the
Stata
Program nnmatch
270
8.4
Examples
271
8.4.1
Matching With Bias-Corrected and Robust Variance Estimators
271
8.4.2
Efficacy Subset Analysis With Matching Estimators
276
8.5
Conclusion
281
У
Propensity Score Analysis With Nonparametric Regression
283
9.1
Overview
284
9.2
Methods of Propensity Score Analysis With Nonparametric Regression
286
9.2.1
The Kernel-Based Matching Estimators
287
9.2.2
Review of the Basic Concepts of Local Linear Regression (lowess)
288
9.2.3
Asymptotic and Finite-Sample Properties of Kernel and Local Linear Matching
297
9.3
Overview of the
Stata
Programs psmatch2 and bootstrap
298
9.4
Examples
299
9.4.1
Analysis of Difference-in-Differences
303
9.4.2
Application of Kernel-Based Matching to One-Point Data
306
9.5
Conclusion
308
1
U
Propensity Score Analysis of Categorical
or Continuous Treatments: Dosage Analyses
309
10.1
Overview
310
10.2
Modeling Doses With a Single Scalar Balancing Score Estimated by an
Ordered Logistic Regression
311
10.3
Modeling Doses With Multiple Balancing Scores Estimated by a
Multinomial Logit Model
313
10.4
The Generalized Propensity Score Estimator
314
10.5
Overview of the
Stata gpscore
Program
321
10.6
Examples
328
10.6.1
Modeling Doses of Treatment With Multiple Balancing Scores
Estimated by a Multinomial Logit Model
328
11
10.6.2
Modeling Doses of Treatment With the
Generalized Propensity Score Estimator
331
10.7
Conclusion
334
Selection Bias and Sensitivity Analysis
335
11.1
Selection Bias: An Overview
336
11.1.1
Sources of Selection Bias
336
11.1.2
Overt Bias Versus Hidden Bias
340
11.1.3
Consequences of Selection Bias
341
11.1.4
Strategies to Correct for Selection Bias
342
11.2
A Monte Carlo Study Comparing Corrective Models
345
11.2.1
Design of the Monte Carlo Study
347
11.2.2
Results of the Monte Carlo Study
353
11.2.3
Implications
356
11.3
Rosenbaum s Sensitivity Analysis
357
11.3.1
The Basic Idea
358
11.3.2
Illustration of Wilcoxon s Signed Rank
Test for Sensitivity Analysis of a Matched Pair Study
359
11.4
Overview of the
Stata
Program rbounds
369
11.5
Examples
377
11.5.1
Sensitivity Analysis of the Effects of Lead Exposure
377
11.5.2
Sensitivity Analysis for the Study Using Pair Matching
378
11.6
Conclusion
379
Concluding Remarks
381
12.1
Common Pitfalls in Observational Studies: A Checklist for Critical Review
381
12.2
Approximating Experiments With Propensity Score Approaches
386
12.2.1
Criticism of Propensity Score Methods
386
12.2.2
Regression and Propensity Score Approaches: Do They Provide Similar Results?
387
12.2.3
Criticism of Sensitivity Analysis
(Г)
390
12.2.4
Group Randomized Trials
390
12.3
Other Advances in Modeling Causality
391
12.4
Directions for Future Development
392
References
395
Index
409
12
|
| any_adam_object | 1 |
| author | Guo, Shenyang Fraser, Mark W. 1946- |
| author_GND | (DE-588)170966216 (DE-588)1017333289 |
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| ctrlnum | (OCoLC)891205307 (DE-599)BVBBV041791721 |
| dewey-full | 519.5/3 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 519 - Probabilities and applied mathematics |
| dewey-raw | 519.5/3 |
| dewey-search | 519.5/3 |
| dewey-sort | 3519.5 13 |
| dewey-tens | 510 - Mathematics |
| discipline | Soziologie Mathematik Wirtschaftswissenschaften Medizin |
| edition | 2. ed. |
| format | Book |
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| id | DE-604.BV041791721 |
| illustrated | Illustrated |
| indexdate | 2024-07-10T01:05:29Z |
| institution | BVB |
| isbn | 9781452235004 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-027237323 |
| oclc_num | 891205307 |
| open_access_boolean | |
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| physical | XXIV, 421 S. graph. Darst. |
| publishDate | 2015 |
| publishDateSearch | 2015 |
| publishDateSort | 2015 |
| publisher | SAGE |
| record_format | marc |
| 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 2. ed. Los Angeles [u.a.] SAGE 2015 XXIV, 421 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Advanced quantitative techniques in the social sciences series [12] Fälschlich als Bd. 11 der Schriftenreihe bezeichnet Includes bibliographical references and index Sozialwissenschaften Social sciences Statistical methods Analysis of variance Statistik (DE-588)4056995-0 gnd rswk-swf Anwendung (DE-588)4196864-5 gnd rswk-swf Statistik (DE-588)4056995-0 s Anwendung (DE-588)4196864-5 s DE-604 Fraser, Mark W. 1946- Verfasser (DE-588)1017333289 aut Advanced quantitative techniques in the social sciences series [12] (DE-604)BV023546702 12 Digitalisierung UB Bamberg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027237323&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 Statistik (DE-588)4056995-0 gnd Anwendung (DE-588)4196864-5 gnd |
| subject_GND | (DE-588)4056995-0 (DE-588)4196864-5 |
| 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 Statistik (DE-588)4056995-0 gnd Anwendung (DE-588)4196864-5 gnd |
| topic_facet | Sozialwissenschaften Social sciences Statistical methods Analysis of variance Statistik Anwendung |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027237323&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 |