Uncertainty in Artificial Intelligence
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Treatment Choice in Heterogeneous Populations Using Experiments without Covariate Data (Invited Paper)
Charles Manski
Abstract:
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.
Keywords: Treatment choice, identification.
Pages: 379-385
PS Link:
PDF Link: /papers/98/p379-manski.pdf
BibTex:
@INPROCEEDINGS{Manski98,
AUTHOR = "Charles Manski ",
TITLE = "Treatment Choice in Heterogeneous Populations Using Experiments without Covariate Data (Invited Paper)",
BOOKTITLE = "Proceedings of the Fourteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-98)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1998",
PAGES = "379--385"
}


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