  I examine the problem of treatment choice when a planner observes (i)
covariates that describe each member of a population of interest and (ii) the
outcomes of an experiment in which subjects randomly drawn from this population
are randomly assigned to treatment groups within which all subjects receive the
same treatment. Covariate data for the subjects of the experiment are not
available. The optimal treatment rule is to divide the population into
subpopulations whose members share the same covariate value, and then to choose
for each subpopulation a treatment that maximizes its mean outcome. However the
planner cannot implement this rule. I draw on my work on nonparametric analysis
of treatment response to address the planner's problem.
