Optimal policy learning for multiaction treatment and risk preference

Giovanni Cerulli

UK Stata Conference 2025 from Stata Users Group

Abstract: I present opl_ma_fb and opl_ma_vf, two community-contributed Stata commands implementing a Rrst-best optimal policy learning (OPL) algorithm to estimate the best treatment assignment given the observation of an outcome, a multiaction (or multiarm) treatment, and a set of observed covariates (features). It allows for different risk preferences in decision making (for example, risk-neutral, risk-averse linear, risk-averse quadratic), and provide graphical representation of the optimal policy, along with an estimate of the maximal welfare (for example, the value-function estimated at optimal policy). A practical example of the use of these commands is provided.

Date: 2025-09-04
References: Add references at CitEc
Citations:

Downloads: (external link)
http://repec.org/lsug2025/
Our link check indicates that this URL is bad, the error code is: 403 Forbidden

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:boc:lsug25:10

Access Statistics for this paper

More papers in UK Stata Conference 2025 from Stata Users Group Contact information at EDIRC.
Bibliographic data for series maintained by Christopher F Baum ().