opl_dt_c: Optimal Policy Learning with Decision Tree

In OPL: Optimal Policy Learning

Optimal Policy Learning with Decision Tree

Description

Implementing ex-ante treatment assignment using as policy class a 2-layer fixed-depth decision-tree at specific splitting variables and threshold values.

Usage

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

Arguments

make_cate_result	A data frame resulting from the make_cate function, containing the predicted treatment effects (my_cate) and other variables for treatment assignment.
z	A character vector containing the names of the variables used for treatment assignment.
w	A string representing the treatment indicator variable name.
c1	Value of the threshold value c1 for the first splitting variable. This number must be chosen between 0 and 1.
c2	Value of the threshold value c2 for the second splitting variable. This number must be chosen between 0 and 1.
c3	Value of the threshold value c3 for the third splitting variable. This number must be chosen between 0 and 1.
verbose	Set TRUE to print the output on the console.

Value

A list containing:

• W_opt_constr: The maximum average constrained welfare.

• W_opt_unconstr: The average unconstrained welfare.

• units_to_be_treated: A data frame of the units to be treated based on the optimal policy.

• 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.