opl_lc_c
In OPL: Optimal Policy Learning

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

library(OPL)

Introduction

The opl_lc_c function implements ex-ante treatment assignment using as policy class a fixed-depth (1-layer) decision-tree at specific splitting variables and threshold values.

Usage

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

Arguments

make_cate_result: A data frame containing input data, including a column named my_cate, representing conditional average treatment effects (CATE).
w: A character string indicating the column name for treatment assignment (binary variable).
policy_constraints: A list of constraints applied to the treatment assignment, such as budget limits or fairness constraints.

Output

The function returns the input data frame augmented with: - treatment_assignment: Binary indicator for treatment assignment based on policy learning. - policy_summary: Summary statistics detailing the constrained optimization results.

Additionally, the function: - Prints a summary of key results, including welfare improvements under the learned policy. - Displays a visualization of the treatment allocation.

Details

The function follows these steps: 1. Estimates the optimal policy assignment using a machine learning-based approach. 2. Incorporates policy constraints to balance fairness, budget, or other practical limitations. 3. Computes and reports key statistics, including constrained welfare gains and proportion of treated units.

Example

# Load example data
set.seed(123)
data_example <- data.frame(
  my_cate = runif(100, -1, 1),
  treatment = sample(0:1, 100, replace = TRUE)")

# Define policy constraints
constraints <- list(budget = 0.5)  # Example: treating at most 50% of units

# Run learning-based constrained policy assignment
result <- opl_lc_c(
  make_cate_result = data_example,
  w = "treatment",
  policy_constraints = constraints
)

Interpretation of Results

The printed summary provides insights into the policy assignment under constraints.
The visualization illustrates the treatment allocation based on CATE estimates.

References

Athey, S., & 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 learning-based constrained 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., & Tetenov, A. (2018). Who Should Be Treated? Empirical Welfare Maximization Methods for Treatment Choice. Econometrica, 86(2), 591–616.

This vignette provides an overview of the opl_lc_c function and demonstrates its usage for learning-based constrained policy assignment. For further details, consult the package documentation.

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.