Optimal policy learning with observational data in multiaction scenarios: Stata implementation

Giovanni Cerulli and Antonio Zinilli

2024 Stata Conference from Stata Users Group

Abstract: This presentation presents a new Stata command for carrying out optimal policy learning (OPL) with observational data, i.e., data-driven optimal decision-making, in multiaction (or multiarm) settings, where a finite set of decision options is available. The presentation and related command focus on three components: estimation, risk preference, and regret estimation via three estimation methods (i.e., regression adjustment, inverse probability weighting, and doubly robust estimators). After briefly presenting the statistical background of this OPL model and the related syntax of the Stata command, the presentation will focus on an application related to climate-related agricultural policies.

Date: 2024-08-04
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http://repec.org/usug2024/US24_Cerulli.pdf

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Persistent link: https://EconPapers.repec.org/RePEc:boc:usug24:11

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