Data-driven decision making using Stata

This presentation focuses on implementing a model in Stata for making optimal decisions in settings with multiple actions or options, commonly known as multi- action (or multi-arm) settings. In these scenarios, a finite set of decision options is available. In the initial part of the presentation, I provide a concise overview of the primary approaches for estimating the reward or value function, as well as the optimal policy within the multi-arm framework. I outline the identification assumptions and statistical properties associated with optimal policy learning estimators. Moving on to the second part, I explore the analysis of decision risk. This examination reveals that the optimal choice can be influenced by the decision maker's risk attitude, specifically regarding the trade-off between the reward conditional mean and conditional variance. The third part of the paper presents a Stata implementation of the model, accompanied by an application to real data.