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A Stable and Efficient Covariate-Balancing Estimator for Causal Survival Effects
May 17, 2024, 4:45 a.m. | Khiem Pham, David A. Hirshberg, Phuong-Mai Huynh-Pham, Michele Santacatterina, Ser-Nam Lim, Ramin Zabih
stat.ML updates on arXiv.org arxiv.org
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.
abstract art arxiv causal challenge data effects estimator independent replace small state stat.me stat.ml survival type
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