Nuisance parameter estimation via honest random forest — NuPa_honest

This function estimates different nuisance parameters using the honest random forest implementation of the 'grf' package

Usage

NuPa_honest_forest(
  NuPa = c("Y.hat", "Y.hat.d", "Y.hat.z", "D.hat", "D.hat.z", "Z.hat"),
  X,
  Y = NULL,
  D = NULL,
  Z = NULL,
  n_cf_folds = 5,
  n_reps = 1,
  cluster = NULL,
  progress = FALSE,
  ...
)

Arguments

NuPa: String vector specifying the nuisance parameters to be estimated. Currently supported: c("Y.hat","Y.hat.d","Y.hat.z","D.hat","D.hat.z","Z.hat")
X: Covariate matrix with N rows and p columns.
Y: Optional numeric vector containing the outcome variable.
D: Optional binary treatment variable.
Z: Optional binary instrumental variable.
n_cf_folds: Number of cross-fitting folds. Default is 5.
n_reps: Number of repetitions of cross-fitting. Default is 1.
cluster: Optional vector of cluster variable if cross-fitting should account for clusters.
progress: If TRUE, progress of nuisance parameter estimation reported.
...: Options passed to the regression_forest.

Value

List of two lists.

predictions contains the requested nuisance parameters
smoothers contains the smoother matrices of requested outcome nuisance parameters
cf_mat Array of dimension n_reps x N x n_cf_folds storing indicators representing the folds used in estimation.

References

Wager, S., & Athey, S. (2018). Estimation and inference of heterogeneous treatment effects using random forests. Journal of the American Statistical Association, 113(523), 1228-1242.