Optimal Policy Learning

Provides functions for optimal policy learning in socioeconomic applications helping users to learn the most effective policies based on data in order to maximize empirical welfare. 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. Documentation about 'OPL' is provided by several international articles via Athey et al (2021, ), Kitagawa et al (2018, ), Cerulli (2022, ), the paper by Cerulli (2021, ) and the book by Gareth et al (2013, ).


Reference manual

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install.packages("OPL")

1.0.2 by Federico Brogi, 2 years ago


Browse source code at https://github.com/cran/OPL


Authors: Federico Brogi [aut, cre] , Barbara Guardabascio [aut] , Giovanni Cerulli [aut]


Documentation:   PDF Manual  


GPL-3 license


Imports stats, dplyr, ggplot2, pander, randomForest, tidyr

Suggests knitr, rmarkdown


See at CRAN