opl_tb_c {OPL}R Documentation

Threshold-based policy learning at specific values

Description

Implementing ex-ante treatment assignment using as policy class a threshold-based (or quadrant) approach at specific threshold values c1 and c2 for respectively the selection variables var1 and var2.

Usage

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

Arguments

make_cate_result

A data frame containing the input data. It must include a column named my_cate representing conditional average treatment effects (CATE) generated using make_cate function.

z

A character vector of length 2 specifying the column names of the two threshold variables to be standardized.

w

A character string specifying the column name indicating treatment assignment (binary variable).

c1

Threshold for var1 given by the user or optimized by the the function. This number must be chosen between 0 and 1.

c2

Threshold for var2 given by the user or optimized by the the function. This number must be chosen between 0 and 1.

verbose

Set TRUE to print the output on the console.

Details

The function:

  1. Standardizes the threshold variables to a 0-1 range.

  2. Identifies the optimal thresholds based on grid search for maximizing constrained welfare.

  3. Computes and displays key statistics, including average welfare measures and the percentage of treated units.

Value

The function invisibly returns the input data frame augmented with the following columns:

Additionally, the function:

References


[Package OPL version 1.0.2 Index]