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Matched Sampling for Causal Effects

Matched Sampling for Causal Effects (3회 대출)

자료유형
단행본
개인저자
Rubin, Donald B.
서명 / 저자사항
Matched Sampling for Causal Effects / Donald B. Rubin.
발행사항
Cambridge ;   New York :   Cambridge University Press,   2006.  
형태사항
ix, 489 p. : ill. ; 24 cm.
ISBN
0521857627 (hardback) 0521674360 (pbk.) 9780521857628 (hardback) 9780521674362 (pbk.)
서지주기
Includes bibliographical references (p. 463-481) and indexes.
일반주제명
Sampling (Statistics). Statistical matching.
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020 ▼a 9780521674362 (pbk.)
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100 1 ▼a Rubin, Donald B.
245 1 0 ▼a Matched Sampling for Causal Effects / ▼c Donald B. Rubin.
260 ▼a Cambridge ; ▼a New York : ▼b Cambridge University Press, ▼c 2006.
300 ▼a ix, 489 p. : ▼b ill. ; ▼c 24 cm.
504 ▼a Includes bibliographical references (p. 463-481) and indexes.
650 0 ▼a Sampling (Statistics).
650 0 ▼a Statistical matching.

No. 소장처 청구기호 등록번호 도서상태 반납예정일 예약 서비스
No. 1 소장처 중앙도서관/서고6층/ 청구기호 001.433 R896m 등록번호 111764095 도서상태 대출가능 반납예정일 예약 서비스 B M
No. 2 소장처 세종학술정보원/인문자료실1/ 청구기호 001.433 R896m 등록번호 151233503 도서상태 대출가능 반납예정일 예약 서비스
No. 소장처 청구기호 등록번호 도서상태 반납예정일 예약 서비스
No. 1 소장처 중앙도서관/서고6층/ 청구기호 001.433 R896m 등록번호 111764095 도서상태 대출가능 반납예정일 예약 서비스 B M
No. 소장처 청구기호 등록번호 도서상태 반납예정일 예약 서비스
No. 1 소장처 세종학술정보원/인문자료실1/ 청구기호 001.433 R896m 등록번호 151233503 도서상태 대출가능 반납예정일 예약 서비스

컨텐츠정보

목차

Part I. The Early Years and the Influence of William G. Cochran: 1. William G. Cochran's contributions to the design, analysis, and evaluation of observational studies; 2. Controlling bias in observational studies: a review William G. Cochran; Part II. Univariate Matching Methods and the Dangers of Regression Adjustment: 3. Matching to remove bias in observational studies; 4. The use of matched sampling and regression adjustment to remove bias in observational studies; 5. Assignment to treatment group on the basis of a covariate; Part III. Basic Theory of Multivariate Matching: 6. Multivariate matching methods that are equal percent bias reducing, I: Some examples; 7. Multivariate matching methods that are equal percent bias reducing, II: Maximums on bias reduction for fixed sample sizes; 8. Using multivariate matched sampling and regression adjustment to control bias in observational studies; 9. Bias reduction using Mahalanobis-metric matching; Part IV. Fundamentals of Propensity Score Matching: 10. The central role of the propensity score in observational studies for causal effects Paul R. Rosenbaum; 11. Assessing sensitivity to an unobserved binary covariate in an observational study with binary outcome Paul R. Rosenbaum; 12. Reducing bias in observational studies using subclassification on the propensity score Paul R. Rosenbaum; 13. Constructing a control group using multivariate matched sampling methods that incorporate the propensity score Paul Rosenbaum; 14. The bias due to incomplete matching Paul R. Rosenbaum; Part V: Affinely Invariant Matching Methods with Ellipsoidally Symmetric Distributions, Theory and Methodology: 15. Affinely invariant matching methods with ellipsoidal distributions Neal Thomas; 16. Characterizing the effect of matching using linear propensity score methods with normal distributions Neal Thomas; 17. Matching using estimated propensity scores: relating theory to practice Neal Thomas; 18. Combining propensity score matching with additional adjustments for prognostic covariates; Part VI. Some Applied Contributions: 19. Causal inference in retrospective studies Paul Holland; 20. The design of the New York school choice scholarships program evaluation Jennifer Hill and Neal Thomas; 21. Estimating and using propensity scores with partially missing data Ralph D'Agostino Jr.; 22. Using propensity scores to help design observational studies: application to the tobacco litigation; Part VII. Some Focused Applications: 23. Criminality, aggression and intelligence in XYY and XXY men H. A. Witkin; 24. Practical implications of modes of statistical inference for causal effects and the critical role of the assignment mechanism; 25. In utero exposure to phenobarbital and intelligence deficits in adult men June Reinisch, Stephanie Sanders, and Erik Mortensen; 26. Estimating causal effects from large data sets using propensity scores; 27. On estimating the causal effects of DNR orders Martin McIntosh.


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