English, Article, Journal or magazine article edition: Inferring Welfare Maximizing Treatment Assignment under Budget Constraints Debopam Bhattacharya; Pascaline Dupas

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Bookmark: http://trove.nla.gov.au/version/122368
Physical Description
  • preprint
Language
  • English

Edition details

Title
  • Inferring Welfare Maximizing Treatment Assignment under Budget Constraints
Author
  • Debopam Bhattacharya
  • Pascaline Dupas
Physical Description
  • preprint
Notes
  • We consider the problem of efficiently allocating a binary treatment among a target population based on a set of discrete and continuous observed characteristics. The goal is to maximize the population mean of an eventual outcome when a budget constraint limits what fraction of the population can be treated. Using sample data resulting from randomized treatment allocation, the ATE conditional on covariates (CATE) is nonparametrically estimated in a first step. The optimal treatment threshold and resulting value function, which are non-smooth functionals of the CATE, are estimated based on sample realizations of the estimated CATE. We derive large-sample distribution theory for these estimates and for the estimated dual value, i.e. the minimum resources needed to attain a specific average outcome via efficient treatment assignment. These inferential methods are applied to the optimal provision of anti-malaria bed nets, using data from a randomized experiment conducted in western Kenya. We find that a government which can afford to distribute subsidized bed nets to only 50% of its target population can, with an efficient allocation rule based on multiple covariates, increase bed-net use by 8 percentage points (25 percent) relative to random allocation and by 4 percentage points (11 percent) relative to one based on wealth only. Our methods can be extended to infer optimal design of eligibility in conditional cash transfer programs.
  • RePEc:nbr:nberwo:14447
Language
  • English
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