TY - ADVS
T1 - grf: Generalized Random Forests
AU - Tibshirani, Julie
AU - Athey, Susan
AU - Sverdrup, Erik
AU - Wager, Stefan
A2 - Friedberg, Rina
A2 - Hadad, Vitor
A2 - Hirshberg, David
A2 - Miner, Luke
A2 - Wright, Marvin
PY - 2025
Y1 - 2025
N2 - Forest-based statistical estimation and inference. GRF provides non-parametric methods for heterogeneous treatment effects estimation (optionally using right-censored outcomes, multiple treatment arms or outcomes, or instrumental variables), as well as least-squares regression, quantile regression, and survival regression, all with support for missing covariates
AB - Forest-based statistical estimation and inference. GRF provides non-parametric methods for heterogeneous treatment effects estimation (optionally using right-censored outcomes, multiple treatment arms or outcomes, or instrumental variables), as well as least-squares regression, quantile regression, and survival regression, all with support for missing covariates
UR - https://CRAN.R-project.org/package=grf
U2 - 10.32614/CRAN.package.grf
DO - 10.32614/CRAN.package.grf
M3 - Software
ER -