ISSN:0162-1459    

DOI:10.1080/01621459.1996.10476901

出版商:Taylor & Francis Group    

年代:1996

数据来源: Taylor

摘要:

We outline a framework for causal inference in settings where assignment to a binary treatment is ignorable, but compliance with the assignment is not perfect so that the receipt of treatment is nonignorable. To address the problems associated with comparing subjects by the ignorable assignment—an “intention-to-treat analysis”—we make use of instrumental variables, which have long been used by economists in the context of regression models with constant treatment effects. We show that the instrumental variables (IV) estimand can be embedded within the Rubin Causal Model (RCM) and that under some simple and easily interpretable assumptions, the IV estimand is the average causal effect for a subgroup of units, the compliers. Without these assumptions, the IV estimand is simply the ratio of intention-to-treat causal estimands with no interpretation as an average causal effect. The advantages of embedding the IV approach in the RCM are that it clarifies the nature of critical assumptions needed for a causal interpretation, and moreover allows us to consider sensitivity of the results to deviations from key assumptions in a straightforward manner. We apply our analysis to estimate the effect of veteran status in the Vietnam era on mortality, using the lottery number that assigned priority for the draft as an instrument, and we use our results to investigate the sensitivity of the conclusions to critical assumptions.

ISSN:0162-1459    

DOI:10.1080/01621459.1996.10476902

出版商:Taylor & Francis Group    

年代:1996

数据来源: Taylor

ISSN:0162-1459    

DOI:10.1080/01621459.1996.10476903

出版商:Taylor & Francis Group    

年代:1996

数据来源: Taylor

ISSN:0162-1459    

DOI:10.1080/01621459.1996.10476904

出版商:Taylor & Francis Group    

年代:1996

数据来源: Taylor

ISSN:0162-1459    

DOI:10.1080/01621459.1996.10476905

出版商:Taylor & Francis Group    

年代:1996

数据来源: Taylor

ISSN:0162-1459    

DOI:10.1080/01621459.1996.10476906

出版商:Taylor & Francis Group    

年代:1996

数据来源: Taylor

ISSN:0162-1459    

DOI:10.1080/01621459.1996.10476907

出版商:Taylor & Francis Group    

年代:1996

数据来源: Taylor

摘要:

Multiple imputation was designed to handle the problem of missing data in public-use data bases where the data-base constructor and the ultimate user are distinct entities. The objective is valid frequency inference for ultimate users who in general have access only to complete-data software and possess limited knowledge of specific reasons and models for nonresponse. For this situation and objective, I believe that multiple imputation by the data-base constructor is the method of choice. This article first provides a description of the assumed context and objectives, and second, reviews the multiple imputation framework and its standard results. These preliminary discussions are especially important because some recent commentaries on multiple imputation have reflected either misunderstandings of the practical objectives of multiple imputation or misunderstandings of fundamental theoretical results. Then, criticisms of multiple imputation are considered, and, finally, comparisons are made to alternative strategies.

ISSN:0162-1459    

DOI:10.1080/01621459.1996.10476908

出版商:Taylor & Francis Group    

年代:1996

数据来源: Taylor

摘要:

Rubin has offered multiple imputation as a general approach to inference from survey data sets with missing values filled in through imputation. In many situations the multiple imputation variance estimator is consistent. In turn, this observation has lent support to a number of complex applications. In fact, however, the multiple imputation variance estimator is inconsistent under some simple conditions. This article extends previous work of Rao and Shao and of Fay directed toward consistent variance estimation under wider conditions. Extensions of Rao and Shao's results tofractionally weighted imputationcombines the estimation efficiency of multiple imputation and the consistency of the Rao—Shao variance estimator.

ISSN:0162-1459    

DOI:10.1080/01621459.1996.10476909

出版商:Taylor & Francis Group    

年代:1996

数据来源: Taylor

摘要:

Unit nonresponse and item nonresponse both occur frequently in surveys. Unit nonresponse is customarily handled by weighting adjustment, whereas item nonresponse is usually treated by some form of imputation. In particular, deterministic or stochastic imputation is often used to assign values for missing item responses. We provide an account of some recent work on jackknife variance estimation based on adjusted imputed values, using only a single imputation and hence a single completed data set. We also present linearized versions of the jackknife variance estimators. We study both stratified simple random sampling and stratified multistage sampling. Existing computer programs for jackknife and linearization variance estimation can be modified to implement the proposed variance estimators without requiring the creation and permanent retention of supplemental data sets. But for secondary analyses, the completed data set must include information on response status for each item as well as on the imputation class.

ISSN:0162-1459    

DOI:10.1080/01621459.1996.10476910

出版商:Taylor & Francis Group    

年代:1996

数据来源: Taylor