Optimal Policy Learning for Multi-Action Treatment and Risk Preference

Session

I present opl_ma_fb and opl_ma_vf, two community-distributed Stata command implementing 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, risk- averse linear, risk-averse quadratic), and provide graphical representation of the optimal policy, along with an estimate of the maximal welfare (i.e., the value- function estimated at optimal policy). A practical example of the use of these commands is provided.