Treatment heterogeneity with right-censored outcomes using grf
Computation
2024-02-27 v3 Applications
Methodology
Abstract
This article walks through how to estimate conditional average treatment effects (CATEs) with right-censored time-to-event outcomes using the function causal_survival_forest (Cui et al., 2023) in the R package grf (Athey et al., 2019, Tibshirani et al., 2024) using data from the National Job Training Partnership Act.
Keywords
Cite
@article{arxiv.2312.02482,
title = {Treatment heterogeneity with right-censored outcomes using grf},
author = {Erik Sverdrup and Stefan Wager},
journal= {arXiv preprint arXiv:2312.02482},
year = {2024}
}
Comments
Software review article prepared for January 2024 ASA Lifetime Data Science newsletter