Causal inference for statistics, social, and biomedical sciences: an introduction
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| Language: | English |
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New York, NY
Cambridge University Press
2015
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| Item Description: | Hier auch später erschienene, unveränderte Nachdrucke |
| Physical Description: | xix, 625 Seiten Diagramme |
| ISBN: | 9780521885881 |
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| 100 | 1 | |a Imbens, Guido |d 1963- |e Verfasser |0 (DE-588)131607545 |4 aut | |
| 245 | 1 | 0 | |a Causal inference for statistics, social, and biomedical sciences |b an introduction |c Guido W. Imbens (Stanford University), Donald B. Rubin (Harvard University) |
| 246 | 1 | 3 | |a Causal inference |
| 264 | 1 | |a New York, NY |b Cambridge University Press |c 2015 | |
| 300 | |a xix, 625 Seiten |b Diagramme | ||
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| 650 | 4 | |a Sozialwissenschaften | |
| 650 | 4 | |a Social sciences |x Research | |
| 650 | 4 | |a Causation | |
| 650 | 4 | |a Inference | |
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| _version_ | 1804152792195006464 |
|---|---|
| adam_text | Contents
Preface
page xvü
PART I INTRODUCTION
1
Causality: The Basic Framework
3
1.1
Introduction
3
1.2
Potential Outcomes
3
1.3
Definition of Causal Effects
5
1.4
Causal Effects in Common Usage
7
1.5
Learning about Causal Effects: Multiple Units
8
1.6
The Stable Unit Treatment Value Assumption
9
1.7
The Assignment Mechanism: An Introduction
13
1.8
Attributes, Pre-Treatment Variables, or Covariates
15
1.9
Potential Outcomes and Lord s Paradox
16
1.10
Causal Estimands
18
1.11
Structure of the Book
20
1.12
Samples, Populations, and Super-Populations
20
1.13
Conclusion
21
Notes
2
1
2
A Brief History of the Potential Outcomes Approach to
Causa! Inference
23
2.1
Introduction
23
2.2
Potential Outcomes and the Assignment Mechanism
before Neyman
24
2.3
Neyman s
(1923)
Potential Outcome Notation in Randomized
Experiments
25
2.4
Earlier Hints for Physical Randomizing
26
2.5
Fisher s
í
1925 )
Proposal to Randomize Treatments to Units
26
2.6
The Observed Outcome Notation in Observational Studies for
Causal Effects
27
2.7
Early Uses of Potential Outcomes in Observational Studies in
Social Sciences
28
VII
Contents
viii
2.8 Potential
Outcomes and the Assignment Mechanism in
Observational Studies: Rubin
(1974)
29
Notes 30
3
A Classification of Assignment Mechanisms
31
3.1
Introduction 31
3.2
Notation 33
3.3
Assignment Probabilities 34
3.4
Restrictions on the Assignment Mechanism
37
3.5
Assignment Mechanisms and Super-Populations
39
3.6
Randomized Experiments
40
3.7
Observational Studies: Regular Assignment Mechanisms
41
3.8
Observational Studies: Irregular Assignment Mechanisms
42
3.9
Conclusion
43
Notes
43
PART II CLASSICAL RANDOMIZED EXPERIMENTS
4
A Taxonomy of Classical Randomized Experiments
47
4.
1 Introduction
47
4.2
Notation
48
4.3
Bernoulli Trials
48
4.4
Completely Randomized Experiments
50
4.5
Stratified Randomized Experiments
51
4.6
Paired Randomized Experiments
52
4.7
Discussion
53
4.8
Conclusion
55
Noies
56
5
Fisher s Exact P-Values for Completely Randomized Experiments
57
5.1
Introduction
57
5.2 The Paul
et al.
Honey Experiment Data
59
5.3
A Simple Example with Six Units
59
5.4
The Choice of Null Hypothesis
63
5_5 The Choice of Statistic
64
5.6 A Small Simulation Study
72
5.7
Interval Estimates Based on Fisher P-Value Calculations
74
5.8 Computation of P-Values
75
5.9
Fisher Exact P-Values with Covariates
78
5.10
Fisher Exact P-Values for the Honey Data
80
5.11
Conclusion
g j
Notes 81
*
Neyman s Repeated
Sampling
Approach to Completely
Randomfawd Experiments g3
6.1
Introduction g3
6.2
The DuAo-Hanna-Ryan Teacher-Incentive Experiment Data
84
6.3
Unbiased Estimation of the Average Treatment Effect
85
Contents
¡χ
6.4
The Sampling Variance of the Neyman Estimator
87
6.5
Estimating the Sampling Variance
92
6.6
Confidence Intervals and Testing
95
6.7
Inference for Population Average Treatment Effects
98
6.8
Neyman s Approach with Covariates
101
6.9
Results for the Duflo-Hanna-Ryan Teacher-Incentive Data
102
6.10
Conclusion
104
Notes
104
Appendix A Sampling Variance Calculations
105
Appendix
В
Random Sampling from a Super-Population
109
7
Regression Methods for Completely Randomized Experiments 1
13
7.1
Introduction
113
7.2
The LRC-CPPT Cholesterol Data
115
7.3
The Super-Population Average Treatment Effects
116
7.4
Linear Regression with No Covariates
118
7.5
Linear Regression with Additional Covariates
122
7.6
Linear Regression with Covariates and Interactions
125
7.7
Transformations of the Outcome Variable
127
7.8
The Limits on Increases in Precision Due to Covariates
128
7.9
Testing for the Presence of Treatment Effects
129
7.10
Estimates for LRC-CPPT Cholesterol Data
131
7.11
Conclusion
133
Notes
134
Appendix
135
8
Model-Based Inference for Completely Randomized Experiments
141
8.1
Introduction
141
8.2
The Lalonde NSW Experimental Job-Training Data
144
8.3
A Simple Example: Naive and More Sophisticated Approaches
to Imputation 1
46
8.4
Bayesian Model-Based Imputation in the Absence of Covariates
150
8.5
Simulation Methods in the Model-Based Approach
163
8.6
Dependence between Potential Outcomes
165
8.7
Model-Based Imputation with Covariates
169
8.8
Super-Population Average Treatment Effects
171
8.9
A Frequentisi
Perspective
172
8.10
Model-Based Estimates of the Effect of the NSW Program
174
8.11
Conclusion 177
Notes l77
Appendix A Posterior Distributions for Normal Models
178
Appendix
В
Analytic Derivations with Known Covariance
Matrix 181
9
Stratified Randomized Experiments
187
9.1
Introduction 187
9.2
The
Tennesee
Project Star Data
188
Contents
χ
9.3
The Structure of Stratified Randomized Experiments
189
9.4
Fisher s Exact P-Values in Stratified Randomized Experiments
192
9.5
The Analysis of Stratified Randomized Experiments from
Neyman s Repeated Sampling Perspective
201
9.6
Regression Analysis of Stratified Randomized Experiments
205
9.7
Model-Based Analysis of Stratified Randomized Experiments
207
9.8
Design Issues: Stratified versus Completely Randomized
ι ι
Experiments
ΔΙΪ
9.9
Conclusion
212
Notes 212
Appendix A: Student-Level Analyses
213
Appendix B: Proofs of Theorems
9.1
and
9.2 214
10
Pairwise Randomized Experiments
219
10.1
Introduction
219
10.2
The Children s Television Workshop Experiment Data
220
10.3
Pairwise Randomized Experiments
220
10.4
Fisher s Exact P-Values in Pairwise Randomized Experiments
222
1
0.5
The Analysis of Pairwise Randomized Experiments from
Neyman s Repeated Sampling Perspective
224
10.6
Regression-Based Analysis of Pairwise Randomized
Experiments
229
10.7
Model-Based Analysis of Pairwise Randomized Experiments
231
10.8
Conclusion
233
Notes
234
Appendix: Proofs
234
1
1
Case Study: An Experimental Evaluation of a Labor Market
Program
240
I
.
I Introduction
240
1
.2
The San Diego SWIM Program Data
240
1
.3
Fisher s Exact P-Values
242
1
.4
Neyman s Repeated Sampling-Based Point Estimates and
Large-Sample Confidence Intervals
245
1
.5
Regression-Based Estimates
247
1
.6
Model-Based Point Estimates
250
1
.7
Conclusion
253
Notes 253
PART III REGULAR ASSIGNMENT MECHANISMS: DESIGN
12
Uncoaftmnded Treatment Assignment
257
12.1
Introduction 257
22. Regular Assignment Mechanisms
258
1
2.3
Balancing Scores and the Propensity Score
266
12.4
Estimation and Inference ~,c
12.5
Design Phase ™
Contents xi
12.6
Assessing Unconfoundedness
278
12.7
Conclusion
279
Notes
279
13
Estimating the Propensity Score
281
13.1
Introduction
281
13.2
The Reinisch
et al. Barbituate
Exposure Data
284
13.3
Selecting the Covariates and Interactions
285
13.4
Choosing the Specification of the Propensity Score for
the Barbituate Data
288
13.5
Constructing Propensity-Score Strata
290
13.6
Choosing Strata for the Barbituate Data
294
13.7
Assessing Balance Conditional on the Estimated
Propensity Score
296
13.8
Assessing Covariate Balance for the Barbituate Data
300
13.9
Conclusion
306
Notes
306
Appendix: Logistic Regression
307
14
Assessing Overlap in Covariate Distributions
309
14.1
Introduction
309
14.2
Assessing Balance in Univariate Distributions
310
14.3
Direct Assessment of Balance in Multivariate Distributions
ЗІЗ
14.4
Assessing Balance in Multivariate Distributions Using the
Propensity Score
314
14.5
Assessing the Ability to Adjust for Differences in Covariates
by Treatment Status
317
14.6
Assessing Balance: Four Illustrations
318
14.7
Sensitivity of Regression Estimates to Lack of Overlap
332
14.8
Conclusion
336
Notes
336
15
Matching to Improve Balance in Covariate Distributions
337
15.1
Introduction
337
15.2
The Reinisch
et al.
Barbituate Exposure Data
339
15.3
Selecting a Subsample of Controls through Matching to
Improve Balance
339
15.4
An Illustration of Propensity Score Matching with Six
Observations
344
15.5
Theoretical Properties of Matching Procedures
345
15.6
Creating Matched Samples for the Barbituate Data
349
15.7
Conclusion
358
Notes
358
16
Trimming to Improve Balance in Covariate Distributions
359
16.1
Introduction
359
16.2
The Right Heart Catheterization Data
360
16.3
An Example with a Single Binary Covariate
362
Contents
xii
1
6.4
Selecting a Subsample Based on the Propensity Score
366
1
6.5
The Optimal Subsample for the Right Heart
Cathetcrization Data 368
16.6
Conclusion 373
Notes 374
PART IV REGULAR ASSIGNMENT MECHANISMS: ANALYSIS
17
Subclassification on the Propensity Score
377
17.1
Introduction
377
17.2
The Imbens-Rubin-Sacerdote Lottery Data
378
17.3
Subclassification on the Propensity Score and Bias Reduction
380
17.4
Subclassification and the Lottery Data
385
17.5
Estimation Based on Subclassification with Additional
Bias Reduction
386
17.6
Neymanian Inference
388
1
7.7
Average Treatment Effects for the Lottery Data
390
17.8
Weighting Estimators and Subclassification
392
17.9
Conclusion
399
Notes
399
18
Matching Estimators
401
18.1
Introduction
401
18.2
The Card-Krueger New Jersey and Pennsylvania Minimum
Wage Data
404
18.3
Exact Matching without Replacement
405
18.4
Inexact Matching without Replacement
407
18.5
Distance Measures
410
18.6
M
atching and the Card-Krueger Data
412
18.7
The Bias of Matching Estimators
415
18.8
Bias-Corrected Matching Estimators
416
18.9
Matching with Replacement
424
18.10
The Number of Matches
425
18.11
Matching Estimators for the Average Treatment Effect for the
Controls and for the Full Sample
427
18.12
Matching Estimates of the Effect of the Minimum Wage
Increase 428
18.13
Conclusion
-,«
Notes 431
I· A General Method for Estimating
Sampling
Variances for
Standard Estimators for Average Causal Effects
19.1
Introduction
19.2
The Imbens-Rubin-Sacerdote Lottery Data
19.3
Estimands A
436
Contents xiii
19.4
The Common Structure of Standard Estimators for Average
Treatment Effects
441
19.5
A General Formula for the Conditional Sampling Variance
445
19.6
A Simple Estimator for the Unit-Level Conditional Sampling
Variance
446
19.7
An Estimator for the Sampling Variance of
τ
Conditional on
Covariates
452
19.8
An Estimator for the Sampling Variance for the Estimator for the
Average Effect for the Treated
452
19.9
An Estimator for the Sampling Variance for the Population
Average Treatment Effect
454
19.10
Alternative Estimators for the Sampling Variance
456
19.11
Conclusion
460
Notes
460
20
Inference for General Causal
Estim
ands
461
20.1
Introduction
461
20.2
The Lalonde NSW Observational Job-Training Data
462
20.3
Causal Estimands
465
20.4
A Model for the Conditional Potential Outcome
Distributions
468
20.5
Implementation
472
20.6
Results for the Lalonde Data
473
20.7
Conclusion
474
Notes
474
PART V REGULAR ASSIGNMENT MECHANISMS: SUPPLEMENTARY
ANALYSES
21
Assessing Unconfoundedness
479
21.1
Introduction
479
21.2
Setup
482
21.3
Estimating Effects on Pseudo-Outcomes
482
21.4
Estimating Effects of Pseudo-Treatments
485
21.5
Robustness to the Set of Pre-Treatment Variables
487
21.6
The Imbens-Rubin-Sacerdote Lottery Data
490
21.7
Conclusion
495
Notes
495
22
Sensitivity Analysis and Bounds
496
22.1
Introduction 496
22.2
The Imbens-Rubin-Sacerdote Lottery
Dala
497
22.3
Bounds 497
22.4
Binary Outcomes: The Rosenbaum-Rubin
Sensitivity Analysis
500
Contents
xiv
22.5
Binary Outcomes: The
Rosenbaum
Sensitivity Analysis
_.> ■ 506
for P-Values
22.6
Conclusion
Notes ^
PART VI REGULAR ASSIGNMENT MECHANISMS WITH
noncompliance: analysis
23
Instrumental Variables Analysis of Randomized Experiments with
One-Sided Noncompliance 513
23.1
Introduction 513
23.2
The Sommer-Zeger Vitamin A Supplement Data
516
23.3
Setup 517
23.4
Intention-to-Treat Effects
519
23.5
Compliance Status
522
23.6
Instrumental Variables
526
23.7
Moment-Based Instrumental Variables Estimators
530
23.8
Linear Models and Instrumental Variables
531
23.9
Naive Analyses: As-Treated
Per Protocol
and Unconfoundedness
535
23.10
Conclusion
539
Notes
539
Appendix
541
24
Instrumental Variables Analysis of Randomized Experiments
with Two-Sided Noncompliance
542
24.1
Introduction
542
24.2
The Angrist Draft Lottery Data
543
24.3
Compliance Status
544
24.4
Intention-to-Treat Effects
546
24.5
Instrumental Variables
548
24.6
Traditional Econometric Methods for Instrumental
Variables
556
24.7
Conclusion
559
Notes
559
25
Model-Based Analysis in Instrumental Variable Settings:
Randomfated Experiments with Two-Sided Noncompliance
560
25.1
Introduction 560
25.2
The McDonald-Hiu Tierney Influenza Vaccination Data
561
25.3
Covariates 567
25.4
Model-Based Instrumental Variables Analyses for Randomized
Experimenis with Two-Sided Noncompliance
568
25.5
Simulation Methods for Obtaining Draws from the Posterior
Distribution of the
Estimând
Given the Data
574
25.6
Models for the Influenza Vaccination Data
578
Contents xv
25.7
Results for the Influenza Vaccination Data
581
25.8
Conclusion
584
Notes
584
PART
VII
CONCLUSION
26
Conclusions and Extensions
589
Notes
590
References
591
Author Index
605
Subject Index
609
|
| any_adam_object | 1 |
| author | Imbens, Guido 1963- Rubin, Donald B. 1943- |
| author_GND | (DE-588)131607545 (DE-588)131607618 |
| author_facet | Imbens, Guido 1963- Rubin, Donald B. 1943- |
| author_role | aut aut |
| author_sort | Imbens, Guido 1963- |
| author_variant | g i gi d b r db dbr |
| building | Verbundindex |
| bvnumber | BV042247805 |
| callnumber-first | H - Social Science |
| callnumber-label | H62 |
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| callnumber-search | H62 |
| callnumber-sort | H 262 |
| callnumber-subject | H - Social Science |
| classification_rvk | SK 840 MR 2100 QH 234 SK 850 |
| classification_tum | MAT 021f |
| ctrlnum | (OCoLC)910377690 (DE-599)BVBBV042247805 |
| dewey-full | 519.5/4 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 519 - Probabilities and applied mathematics |
| dewey-raw | 519.5/4 |
| dewey-search | 519.5/4 |
| dewey-sort | 3519.5 14 |
| dewey-tens | 510 - Mathematics |
| discipline | Soziologie Mathematik Wirtschaftswissenschaften |
| format | Book |
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| id | DE-604.BV042247805 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-10T01:16:20Z |
| institution | BVB |
| isbn | 9780521885881 |
| language | English |
| lccn | 014020988 |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-027685835 |
| oclc_num | 910377690 |
| open_access_boolean | |
| owner | DE-473 DE-BY-UBG DE-703 DE-384 DE-91G DE-BY-TUM DE-11 DE-19 DE-BY-UBM DE-188 DE-N2 DE-739 DE-824 DE-858 DE-83 DE-355 DE-BY-UBR DE-634 |
| owner_facet | DE-473 DE-BY-UBG DE-703 DE-384 DE-91G DE-BY-TUM DE-11 DE-19 DE-BY-UBM DE-188 DE-N2 DE-739 DE-824 DE-858 DE-83 DE-355 DE-BY-UBR DE-634 |
| physical | xix, 625 Seiten Diagramme |
| publishDate | 2015 |
| publishDateSearch | 2015 |
| publishDateSort | 2015 |
| publisher | Cambridge University Press |
| record_format | marc |
| spelling | Imbens, Guido 1963- Verfasser (DE-588)131607545 aut Causal inference for statistics, social, and biomedical sciences an introduction Guido W. Imbens (Stanford University), Donald B. Rubin (Harvard University) Causal inference New York, NY Cambridge University Press 2015 xix, 625 Seiten Diagramme txt rdacontent n rdamedia nc rdacarrier Hier auch später erschienene, unveränderte Nachdrucke Sozialwissenschaften Social sciences Research Causation Inference Kausalanalyse (DE-588)4163511-5 gnd rswk-swf Kausalanalyse (DE-588)4163511-5 s DE-604 Rubin, Donald B. 1943- Verfasser (DE-588)131607618 aut Erscheint auch als Online-Ausgabe 978-1-139-02575-1 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=027685835&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Imbens, Guido 1963- Rubin, Donald B. 1943- Causal inference for statistics, social, and biomedical sciences an introduction Sozialwissenschaften Social sciences Research Causation Inference Kausalanalyse (DE-588)4163511-5 gnd |
| subject_GND | (DE-588)4163511-5 |
| title | Causal inference for statistics, social, and biomedical sciences an introduction |
| title_alt | Causal inference |
| title_auth | Causal inference for statistics, social, and biomedical sciences an introduction |
| title_exact_search | Causal inference for statistics, social, and biomedical sciences an introduction |
| title_full | Causal inference for statistics, social, and biomedical sciences an introduction Guido W. Imbens (Stanford University), Donald B. Rubin (Harvard University) |
| title_fullStr | Causal inference for statistics, social, and biomedical sciences an introduction Guido W. Imbens (Stanford University), Donald B. Rubin (Harvard University) |
| title_full_unstemmed | Causal inference for statistics, social, and biomedical sciences an introduction Guido W. Imbens (Stanford University), Donald B. Rubin (Harvard University) |
| title_short | Causal inference for statistics, social, and biomedical sciences |
| title_sort | causal inference for statistics social and biomedical sciences an introduction |
| title_sub | an introduction |
| topic | Sozialwissenschaften Social sciences Research Causation Inference Kausalanalyse (DE-588)4163511-5 gnd |
| topic_facet | Sozialwissenschaften Social sciences Research Causation Inference Kausalanalyse |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027685835&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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