Optimal policy learning using Stata

This presentation 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, the package implements three popular policy learning algorithms in Stata (threshold-based, linear-combination, and Fxed-depth decision tree), and provides practical demonstrations of them using a real database. Also, I present a policy scenario development proposing a menu strategy, which is particularly useful when selection variables are affected by welfare monotonicity. Overall, the package contributes to bridging the gap between theoretical advancements and practical applications of policy learning.