OPL - Stata package for optimal policy learning

Syntax

command ... [, options]

command

make_cate Predicting conditional average treatment effect (CATE)

opl_tb Threshold-based optimal policy learning

opl_tb_c Threshold-based policy learning at specific threshold values

opl_lc Linear-combination optimal policy learning

opl_lc_c Linear-combination policy learning at specific parameters' values

opl_dt Decision-tree optimal policy learning

opl_dt_c Decision-tree policy learning at specific splitting variables and threshold values

Description

OPL is a package for learning optimal policies from data for empirical welfare maximization.
Specifically, OPL allows to find "treatment assignment rules" that maximize the overall welfare, defined as the sum of
the policy effects estimated over all the policy beneficiaries.
OPL learns the optimal policy empirically, i.e. based on data and observations obtained from previous (same or similar) implemented policies.
OPL carries out empirical welfare maximization within three policy classes: (i) Threshold-based; (ii) Linear-combination; and
(iii) Decision-tree.

Empirical welfare maximization requires the estimation of the Conditional
Average Treatment Effect (CATE) of the past policy. Currently, OPL estimates CATE via
linear and non-linear Regression Adjustment (RA), allowing for the target outcome to be continuous, binary, count, or fractional.
The treatment variable of reference must be binary 0/1.

References

Athey, S., and Wager S. 2021. Policy Learning with Observational Data, Econometrica, 89, 1, 133–161.

Cerulli, G. 2021. Improving econometric prediction by machine learning, Applied Economics Letters, 28, 16, 1419-1425.

Cerulli, G. 2022. Optimal treatment assignment of a threshold-based policy: empirical protocol and related issues, Applied Economics Letters, 30, 8, 1010-1017.

Cerulli, G. 2023. Fundamentals of Supervised Machine Learning: With Applications in Python, R, and Stata, Springer.

Cerulli, G. 2024. Optimal Policy Learning using Stata. Zenodo. DOI: https://doi.org/10.5281/zenodo.10822240.

Gareth, J., Witten, D., Hastie, D.T., Tibshirani, R. 2013. An Introduction to Statistical Learning: with Applications in R. New York, Springer.

Kitagawa, T., and A. Tetenov. 2018. Who Should Be Treated? Empirical Welfare Maximization Methods for Treatment Choice, Econometrica, 86, 2, 591–616.

Acknowledgment

The development of this software was supported by FOSSR (Fostering Open Science in Social Science Research), a project funded by the European Union - NextGenerationEU under the NPRR Grant agreement n. MURIR0000008.

Author

Giovanni Cerulli

IRCrES-CNR

Research Institute for Sustainable Economic Growth, National Research Council of Italy

E-mail: giovanni.cerulli@ircres.cnr.it