opl_dt_c

Introduction

The opl_dt_c function implements 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)

Details

The opl_dt_c function follows these steps: 1. Standardizes selection variables. 2. Implements a grid search over threshold values. 3. Identifies the optimal constrained policy maximizing welfare. 4. Computes summary statistics and visualizes treatment assignment.

Example

# Example data
data_example <- data.frame(
  my_cate = runif(100, -1, 1),
  X1 = runif(100, 0, 1),
  X2 = runif(100, 0, 1),
  treatment = sample(0:1, 100, replace = TRUE)
)

# Run the decision tree-based policy learning function
opl_dt_c()

Interpretation of Results

• The printed summary provides insights into constrained policy learning outcomes.
• The generated plot visualizes the treatment allocation under the optimal decision tree-based policy.

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_dt_c function and demonstrates its usage for decision tree-based policy learning. For further details, consult the package documentation.