opl_lc

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opl_lc_c

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.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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