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## Causal inference for statistics, social, and biomedical sciences : an introduction (11회 대출)

자료유형
단행본
개인저자
Imbens, Guido. Rubin, Donald B.
서명 / 저자사항
Causal inference for statistics, social, and biomedical sciences : an introduction / Guido W. Imbens, Donald B. Rubin.
발행사항
New York :   Cambridge University Press,   2015.
형태사항
xix, 625 p. : ill. ; 26 cm.
ISBN
9780521885881
서지주기
Includes bibliographical references and index.
일반주제명
Social sciences --Research. Causation. Inference.
 000 00000cam u2200205 a 4500 001 000045837468 005 20150716151616 008 150630s2015 nyua b 001 0 eng d 010 ▼a 2014020988 020 ▼a 9780521885881 035 ▼a (KERIS)REF000017490155 040 ▼a DLC ▼b eng ▼c DLC ▼e rda ▼d 211009 050 0 0 ▼a H62 ▼b .I537 2014 082 0 0 ▼a 519.5/4 ▼2 23 084 ▼a 519.54 ▼2 DDCK 090 ▼a 519.54 ▼b I32c 100 1 ▼a Imbens, Guido. 245 1 0 ▼a Causal inference for statistics, social, and biomedical sciences : ▼b an introduction / ▼c Guido W. Imbens, Donald B. Rubin. 260 ▼a New York : ▼b Cambridge University Press, ▼c 2015. 300 ▼a xix, 625 p. : ▼b ill. ; ▼c 26 cm. 504 ▼a Includes bibliographical references and index. 650 0 ▼a Social sciences ▼x Research. 650 0 ▼a Causation. 650 0 ▼a Inference. 700 1 ▼a Rubin, Donald B. 945 ▼a KLPA

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### 컨텐츠정보

#### 목차

CONTENTS
Preface = xvii
PART �� 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 = 21
2 A Brief History of the Potential Outcomes Approach to Causal 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(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
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 �� 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
Notes = 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 = 81
Notes = 81
6 Neyman's Repeated Sampling Approach to Completely Randomized Experiments = 83
6.1 Introduction = 83
6.2 The Duflo-Hanna-Ryan Teacher-Incentive Experiment Data = 84
6.3 Unbiased Estimation of the Average Treatment Effect = 85
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 B : Random Sampling from a Super-Population = 109
7 Regression Methods for Completely Randomized Experiments = 113
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 = 146
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 Frequentist Perspective = 172
8.10 Model-Based Estimates of the Effect of the NSW Program = 174
8.11 Conclusion = 177
Notes = 177
Appendix A : Posterior Distributions for Normal Models = 178
Appendix B : Analytic Derivations with Known Covariance Matrix = 181
9 Stratified Randomized Experiments = 187
9.1 Introduction = 187
9.2 The Tennesee Project Star Data = 188
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 Experiments = 211
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
10.5 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
11 Case Study : An Experimental Evaluation of a Labor Market Program = 240
11.1 Introduction = 240
11.2 The San Diego SWIM Program Data = 240
11.3 Fisher's Exact P-Values = 242
11.4 Neyman's Repeated Sampling-Based Point Estimates and Large-Sample Confidence Intervals = 245
11.5 Regression-Based Estimates = 247
11.6 Model-Based Point Estimates = 250
11.7 Conclusion = 253
Notes = 253
PART �� REGULAR ASSIGNMENT MECHANISMS : DESIGN
12 Unconfounded Treatment Assignment = 257
12.1 Introduction = 257
12.2 Regular Assignment Mechanisms = 258
12.3 Balancing Scores and the Propensity Score = 266
12.4 Estimation and Inference = 268
12.5 Design Phase = 276
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 = 313
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
16.4 Selecting a Subsample Based on the Propensity Score = 366
16.5 The Optimal Subsample for the Right Heart Catheterization Data = 368
16.6 Conclusion = 373
Notes = 374
PART �� 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
17.7 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 Matching 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 = 430
Notes = 431
19 A General Method for Estimating Sampling Variances for Standard Estimators for Average Causal Effects = 433
19.1 Introduction = 433
19.2 The Imbens-Rubin-Sacerdote Lottery Data = 435
19.3 Estimands = 436
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 [TEX]$$\hat{\tau}$$[/TEX] 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 Estimands = 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 �� 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 Data = 497
22.3 Bounds = 497
22.4 Binary Outcomes : The Rosenbaum-Rubin Sensitivity Analysis = 500
22.5 Binary Outcomes : The Rosenbaum Sensitivity Analysis for P-Values = 506
22.6 Conclusion = 509
Notes = 509
PART �� 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 : Randomized 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 Experiments with Two-Sided Noncompliance = 568
25.5 Simulation Methods for Obtaining Draws from the Posterior Distribution of the Estimand Given the Data = 574
25.6 Models for the Influenza Vaccination Data = 578
25.7 Results for the Influenza Vaccination Data = 581
25.8 Conclusion = 584
Notes = 584
PART �� CONCLUSION
26 Conclusions and Extensions = 589
Notes = 590
References = 591
Author Index = 605
Subject Index = 609

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