Causal Inference in Statistics, Social, and Biomedical Sciences

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Cambridge University Press, 06.04.2015 - 625 Seiten
Most questions in social and biomedical sciences are causal in nature: what would happen to individuals, or to groups, if part of their environment were changed? In this groundbreaking text, two world-renowned experts present statistical methods for studying such questions. This book starts with the notion of potential outcomes, each corresponding to the outcome that would be realized if a subject were exposed to a particular treatment or regime. In this approach, causal effects are comparisons of such potential outcomes. The fundamental problem of causal inference is that we can only observe one of the potential outcomes for a particular subject. The authors discuss how randomized experiments allow us to assess causal effects and then turn to observational studies. They lay out the assumptions needed for causal inference and describe the leading analysis methods, including, matching, propensity-score methods, and instrumental variables. Many detailed applications are included, with special focus on practical aspects for the empirical researcher.
 

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Inhalt

A Brief History of the Potential Outcomes Approach
23
A Classification of Assignment Mechanisms
31
Neymans 1923 Potential Outcome Notation in Randomized
47
Fishers Exact PValues for Completely Randomized Experiments
57
Neymans Repeated Sampling Approach to Completely
83
Regression Methods for Completely Randomized Experiments
113
3
123
ModelBased Inference for Completely Randomized Experiments
141
Estimating the Propensity Score
281
Assessing Overlap in Covariate Distributions
309
Matching to Improve Balance in Covariate Distributions
337
Trimming to Improve Balance in Covariate Distributions
359
Subclassification on the Propensity Score
377
Matching Estimators
401
A General Method for Estimating Sampling Variances
433
Inference for General Causal Estimands
461

15
145
18
156
Stratified Randomized Experiments
187
2
215
Pairwise Randomized Experiments
219
An Experimental Evaluation of a Labor Market
240
24
251
Unconfounded Treatment Assignment
257
Assessing Unconfoundedness
479
Sensitivity Analysis and Bounds
496
Instrumental Variables Analysis of Randomized Experiments with
513
Instrumental Variables Analysis of Randomized Experiments
542
Conclusions and Extensions
589
Author Index
605
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Über den Autor (2015)

Guido W. Imbens is Professor of Economics at the Graduate School of Business, Stanford University. He has held tenured faculty positions at Harvard University, the University of California, Los Angeles, the University of California, Berkeley, and Stanford University. He is a fellow of the Econometric Society and the American Academy of Arts and Sciences. Imbens has published widely in economics and statistics journals, including Econometrica, The American Economic Review, the Annals of Statistics, the Journal of the American Statistical Association, Biometrika, and the Journal of the Royal Statistical Society.

Donald B. Rubin is John L. Loeb Professor of Statistics at Harvard University, where he has been professor since 1983 and department chair for thirteen of those years. He has authored or coauthored nearly four hundred publications (including ten books), has four joint patents, and has made important contributions to statistical theory and methodology, particularly in causal inference, design and analysis of experiments and sample surveys, treatment of missing data, and Bayesian data analysis. Rubin has received the Samuel S. Wilks Medal from the American Statistical Association, the Parzen Prize for Statistical Innovation, the Fisher Lectureship, and the George W. Snedecor Award from the Committee of Presidents of Statistical Societies. He was named Statistician of the Year by the American Statistical Association, Boston and Chicago chapters. He is one of the most highly cited authors in mathematics and economics with nearly 150,000 citations to date.

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