Treatment choice under ambiguity induced by inferential problems

Charles Manski*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

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 languageEnglish (US)
Pages (from-to)67-82
Number of pages16
JournalJournal of Statistical Planning and Inference
Volume105
Issue number1
DOIs
StatePublished - Jun 15 2002

Keywords

  • Bounds
  • Identification
  • Statistical treatment rules
  • Treatment response

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty
  • Applied Mathematics

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