Optimal Policy Learning for Multi-Action Treatment with Risk Preference using Stata

This paper presents the Stata community-distributed command "oplₘafb" (and the companion command "oplₘaᵥf"), for implementing the first-best Optimal Policy Learning (OPL) algorithm to estimate the best treatment assignment given the observation of an outcome, a multi-action (or multi-arm) treatment, and a set of observed covariates (features). It allows for different risk preferences in decision-making (i. e. , risk-neutral, linear risk-averse, and quadratic risk-averse), and provides a graphical representation of the optimal policy, along with an estimate of the maximal welfare (i. e. , the value-function estimated at optimal policy) using regression adjustment (RA), inverse-probability weighting (IPW), and doubly robust (DR) formulas.