Analysis of randomized experiments with missing covariate and outcome data is problematic because the population parameters of interest are not identified unless one makes untestable assumptions about the distribution of the missing data. This paper shows how population parameters can be bounded without making untestable distributional assumptions. The bounds are sharp; that is, they exhaust all of the information that is available from the data. The bounds are illustrated with an application to data obtained from a clinical trial of treatments for hypertension. The bounds are sufficiently narrow to permit substantive conclusions to be drawn about the effects of different treatments.
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Paper provided by University of Iowa, Department of Economics in its series Working Papers with number
97-16.
Length: 34 Pages Date of creation: Dec 1997 Date of revision: Handle: RePEc:uia:iowaec:97-16
Contact details of provider: Postal: University of Iowa, Department of Economics, Henry B. Tippie College of Business, Iowa City, Iowa 52242 Phone: (319) 335-0829 Fax: (319) 335-1956 Web page: http://tippie.uiowa.edu/economics/ More information through EDIRC
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Find related papers by JEL classification: C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Estimation C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Semiparametric and Nonparametric Methods
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