Regret Analysis in Threshold Policy Design

Federico Crippa

Papers from arXiv.org

Abstract: Threshold policies are targeting mechanisms that assign treatments based on whether an observable characteristic exceeds a certain threshold. They are widespread across multiple domains, such as welfare programs, taxation, and clinical medicine. This paper addresses the problem of designing threshold policies using experimental data, when the goal is to maximize the population welfare. First, I characterize the regret (a measure of policy optimality) of the Empirical Welfare Maximizer (EWM) policy, popular in the literature. Next, I introduce the Smoothed Welfare Maximizer (SWM) policy, which improves the EWM's regret convergence rate under an additional smoothness condition. The two policies are compared studying how differently their regrets depend on the population distribution, and investigating their finite sample performances through Monte Carlo simulations. In many contexts, the welfare guaranteed by the novel SWM policy is larger than with the EWM. An empirical illustration demonstrates how the treatment recommendation of the two policies may in practice notably differ.

Date: 2024-04
New Economics Papers: this item is included in nep-pbe
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://arxiv.org/pdf/2404.11767 Latest version (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2404.11767

Access Statistics for this paper

More papers in Papers from arXiv.org
Bibliographic data for series maintained by arXiv administrators ().