opl_lc_c: Linear Combination Based Policy Learning

In OPL: Optimal Policy Learning

Linear Combination Based Policy Learning

Description

Implementing ex-ante treatment assignment using as policy class a linear-combination approach at specific parameters' values c1, c2, and c3 for the linear-combination of variables var1 and var2: c1var1+c2var2>=c3.

Usage

opl_lc_c(make_cate_result, z, w, c1 = NA, c2 = NA, c3 = NA, verbose = TRUE)

Arguments

make_cate_result	A data frame containing the input data. It must include a column named my_cate representing conditional average treatment effects (CATE) generated using make_cate function.
z	A character vector of length 2 specifying the column names of the two threshold variables to be standardized.
w	A character string specifying the column name indicating treatment assignment (binary variable).
c1	Threshold for var1 given by the user or optimized by the the function. This number must be chosen between 0 and 1.
c2	Threshold for var2 given by the user or optimized by the the function. This number must be chosen between 0 and 1.
c3	Third parameter of the linear-combination. This number must be chosen between 0 and 1.
verbose	Set TRUE to print the output on the console.

Details

The function performs the following steps:

• Standardizes the threshold variables using a min-max scaling technique.

• Determines the optimal treatment assignment based on the linear combination of the threshold variables.

• Performs a grid search to estimate the optimal policy.

• Outputs a plot visualizing the optimal treatment assignments.

• Prints the main results, including the percentage of treated units, the unconstrained and constrained welfare, and the policy parameters.

Value

The function returns a data frame containing the standardized variables and treatment assignments, and prints a summary of the results and a plot showing the optimal policy assignment.

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, DOI: 10.1080/13504851.2022.2032577.

• 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.

OPL documentation built on April 4, 2025, 3:09 a.m.