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OPL (version 1.0.2)

Optimal Policy Learning

Description

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, ).

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Version

Install

install.packages('OPL')

Monthly Downloads

138

Version

1.0.2

License

GPL-3

Maintainer

Federico Brogi

Last Published

February 27th, 2025

Functions in OPL (1.0.2)

opl_lc_c

Linear Combination Based Policy Learning
opl_tb_c

Threshold-based policy learning at specific values
make_cate

Function to calculate the Causal Treatment Effect
overlapping

Testing overlap between old and new policy sample
OPL

OPL: Optimal Policy Learning Package
opl_dt_max_choice

User selection on multiple choice
opl_dt_c

Optimal Policy Learning with Decision Tree