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Optimal policy learning for multiaction treatment and risk preference

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  • Giovanni Cerulli

    (CNR–IRCRES, National Research Council of Italy)

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.

Suggested Citation

  • Giovanni Cerulli, 2025. "Optimal policy learning for multiaction treatment and risk preference," UK Stata Conference 2025 10, Stata Users Group.
    • Handle: RePEc:boc:lsug25:10
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