Nonparametric Analysis of Randomized Experiments With Missing Covariate and Outcome Data
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.
To our knowledge, this item is not available for
download. To find whether it is available, there are three
1. Check below under "Related research" whether another version of this item is available online.
2. Check on the provider's web page whether it is in fact available.
3. Perform a search for a similarly titled item that would be available.
|Date of creation:||Dec 1997|
|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
When requesting a correction, please mention this item's handle: RePEc:uia:iowaec:97-16. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (John Solow)
If references are entirely missing, you can add them using this form.