Tidy helper function for causal_forest function

Splits the data at random into a training and a test set, fits grf::causal_forest() on the training set, and predicts each unit's treatment effect. The data must contain the outcome as Y and the treatment as Z.

Usage

causal_forest_handler(data, covariate_names, share_train = 0.5, ...)

Arguments

data

A data frame with columns Y, Z, and the covariates named in covariate_names.

covariate_names

A character vector naming the covariate columns the forest may split on.

share_train

The share of units assigned to the training set. Defaults to 0.5.

...

Further arguments passed to grf::causal_forest().

Value

data with four columns added: pred (the predicted treatment effect), var_imp (the position in covariate_names of the most important covariate), and the logical indicators train and test.

Details

See https://book.declaredesign.org/complex-designs.html#discovery-using-causal-forests

Examples

library(DeclareDesign)
#> Loading required package: randomizr
#> Loading required package: fabricatr
#> Loading required package: estimatr
library(ggplot2)
#> 
#> Attaching package: ‘ggplot2’
#> The following object is masked from ‘package:DeclareDesign’:
#> 
#>     vars

dat <- fabricate(
   N = 1000,
   A = rnorm(N),
   B = rnorm(N),
   Z = complete_rs(N),
   Y = A*Z + rnorm(N))

# note: remove num.threads = 1 to use more processors
estimates <- causal_forest_handler(data = dat, covariate_names = c("A", "B"), num.threads = 1)

ggplot(data = estimates, aes(A, pred)) + geom_point()