A Stable and Efficient Covariate-Balancing Estimator for Causal Survival Effects
Methodology
2024-05-17 v2 Machine Learning
Abstract
We propose an empirically stable and asymptotically efficient covariate-balancing approach to the problem of estimating survival causal effects in data with conditionally-independent censoring. This addresses a challenge often encountered in state-of-the-art nonparametric methods: the use of inverses of small estimated probabilities and the resulting amplification of estimation error. We validate our theoretical results in experiments on synthetic and semi-synthetic data.
Cite
@article{arxiv.2310.02278,
title = {A Stable and Efficient Covariate-Balancing Estimator for Causal Survival Effects},
author = {Khiem Pham and David A. Hirshberg and Phuong-Mai Huynh-Pham and Michele Santacatterina and Ser-Nam Lim and Ramin Zabih},
journal= {arXiv preprint arXiv:2310.02278},
year = {2024}
}
Comments
32 pages, 5 figures