make_cate: Function to calculate the Causal Treatment Effect

In OPL: Optimal Policy Learning

Function to calculate the Causal Treatment Effect

Description

Predicting conditional average treatment effect (CATE) on a new policy based on the training over an old policy

Usage

make_cate(
  model,
  train_data,
  test_data,
  w,
  x,
  y,
  family = gaussian(),
  ntree = 100,
  mtry = 2,
  verbose = TRUE
)

Arguments

model	A model object used for estimation.
train_data	The training dataset.
test_data	The test dataset.
w	Set the treatment variable.
x	set Independent variables for the model.
y	Set the outcome variable.
family	The family type for the model (e.g., 'binomial').
ntree	Number of trees for the Random Forest model.
mtry	Number of variables to consider at each tree split in the Random Forest model.
verbose	Set TRUE to print the output on the console.

Value

An object containing the estimated causal treatment effect results.

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.