Optimal policy learning using Stata
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| Title: | Optimal policy learning using Stata |
|---|---|
| Authors: | Cerulli, Giovanni |
| Source: | The Stata Journal: Promoting communications on statistics and Stata. 25:309-343 |
| Publisher Information: | SAGE Publications, 2025. |
| Publication Year: | 2025 |
| Subject Terms: | Optimal policy learning, Machine learning, Optimal treatment assignment, Ex-ante policy evaluation |
| Description: | 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. |
| Document Type: | Article Research |
| Language: | English |
| ISSN: | 1536-8734 1536-867X |
| DOI: | 10.1177/1536867x251341143 |
| DOI: | 10.5281/zenodo.10822239 |
| DOI: | 10.5281/zenodo.10822240 |
| Rights: | CC BY URL: https://journals.sagepub.com/page/policies/text-and-data-mining-license |
| Accession Number: | edsair.doi.dedup.....3ce9aa0505f7062e674282e67361c8ae |
| Database: | OpenAIRE |
Nájsť tento článok vo Web of Science | Abstract: | 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. |
|---|---|
| ISSN: | 15368734 1536867X |
| DOI: | 10.1177/1536867x251341143 |