Abstract
This paper describes the author's research connecting the empirical analysis of treatment response with the normative analysis of treatment choice under ambiguity. Imagine a planner who must choose a treatment rule assigning a treatment to each member of a heterogeneous population of interest. The planner observes certain covariates for each person. Each member of the population has a response function mapping treatments into a real-valued outcome of interest. Suppose that the planner wants to choose a treatment rule that maximizes the population mean outcome. An optimal rule assigns to each member of the population a treatment that maximizes mean outcome conditional on the person's observed covariates. However, identification problems in the empirical analysis of treatment response commonly prevent planners from knowing the conditional mean outcomes associated with alternative treatments; hence planners commonly face problems of treatment choice under ambiguity. The research surveyed here characterizes this ambiguity in practical settings where the planner may be able to bound but not identify the relevant conditional mean outcomes. The statistical problem of treatment choice using finite-sample data is discussed as well.
Original language | English (US) |
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Pages (from-to) | 67-82 |
Number of pages | 16 |
Journal | Journal of Statistical Planning and Inference |
Volume | 105 |
Issue number | 1 |
DOIs | |
State | Published - Jun 15 2002 |
Funding
The research described here was supported in part by National Science Foundation grants SBR-9722846 and SES-0001436. I have benefitted from the constructive comments of the ISIPTA participants and the reviewers of this paper.
Keywords
- Bounds
- Identification
- Statistical treatment rules
- Treatment response
ASJC Scopus subject areas
- Statistics and Probability
- Statistics, Probability and Uncertainty
- Applied Mathematics