A Stable and Efficient Covariate-Balancing Estimator for Causal Survival Effects

2023/10/01 by Pham, Khiem, Hirshberg, David A., Huynh-Pham, Phuong-Mai +3
#FOS: Computer and information sciences #Machine Learning (stat.ML) #Methodology (stat.ME)

paper · doi:10.48550/arxiv.2310.02278

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.

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