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Optimal policy learning using Stata
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DOI：10.1177/1536867x251341143.png)
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Abstract
En 中文
In this article, I introduce the 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 fixed-depth decision tree—and provides practical demonstrations of them using a real dataset. I also present policy-scenario development proposing a menu strategy, which is particularly useful when selection variables are affected by welfare monotonicity. Overall, this article contributes to bridging the gap between theoretical advancements and practical applications in the field of policy learning.
Keywords:
optimal policy learning
Stata
policy impact evaluation
decision tree
welfare monotonicity
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