Optimal policy learning using Stata

Giovanni Cerulli

2023 Stata Conference from Stata Users Group

Abstract: In the footsteps of the recent literature on empirical welfare maximization (EWM), I present a new Stata command called opl to carry out "optimal policy learning", a statistical procedure to design treatment assignments using a machine learning approach. The opl command focuses on three policy classes: threshold based, linear combination, and fixed-depth tree. I show a practical example, based on a real policy case—that is, the popular LaLonde training program—where, by stressing the policymaker perspective, I show how to carry out optimal treatment assignment and the potential operative problems that can come up in applying this procedure to real-world case studies. I will discuss, in particular, problems of “angle solutions”. The presentation offers a general protocol to carry out optimal policy assignment using Stata and contributes to stress the policymaker empirical perspective and related issues arising when carrying out optimal policy assignment in practice.

Date: 2023-07-29
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http://repec.org/usug2023/US23_Cerulli.pdf

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Working Paper: Optimal policy learning using Stata (2024)
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Persistent link: https://EconPapers.repec.org/RePEc:boc:usug23:12

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