"Nonparametric Analysis of Randomized Experiments with Missing
Covariate and Outcome Data"

Joel Horowitz

ABSTRACT

	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 misssing data.  This paper shows how
population parameters can be bounded without making untestable
distributional assumptions.  Bounds are also derived under the
assumption that covariate data are missing completely at random.  In
each case the bounds are sharp; they exhaust all of the information
that is available given the data and the maintained assumptions.  The
bounds are illustrated with applications to data obtained from a
clinical trial and data relating family structure to the probability
that a youth graduates from high school.