NuPa_honest_forest {OutcomeWeights} | R Documentation |
Nuisance parameter estimation via honest random forest
Description
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: |
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 |
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.