Optimal Policy Learning using Stata

Abstract

This article introduces the Stata package OPL for optimal policy learning, facilitating ex-ante policy impact evaluation within the Stata environment. Despite theoretical progress, practical implementations of policy learning algorithms are still poor within popular statistical software. To address this limitation, OPL implements three popular policy learning algorithms in Stata { threshold-based, linear-combination, and  xed-depth decision tree { and provides practical demonstrations of them using a real dataset. Also, the paper presents policy scenario development proposing a menu strategy, particularly useful when selection variables are a ected by welfare monotonicity. Overall, the paper contributes to bridging the gap between theoretical advancements and practical applications in the eld of policy learning.