A Stable and Efficient Covariate-Balancing Estimator for Causal Survival Effects
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arXiv
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| Hauptverfasser: | , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2023
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| _version_ | 1866909204716978176 |
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| author | Pham, Khiem Hirshberg, David A. Huynh-Pham, Phuong-Mai Santacatterina, Michele Lim, Ser-Nam Zabih, Ramin |
| author_facet | Pham, Khiem Hirshberg, David A. Huynh-Pham, Phuong-Mai Santacatterina, Michele Lim, Ser-Nam Zabih, Ramin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_02278 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | A Stable and Efficient Covariate-Balancing Estimator for Causal Survival Effects Pham, Khiem Hirshberg, David A. Huynh-Pham, Phuong-Mai Santacatterina, Michele Lim, Ser-Nam Zabih, Ramin Methodology Machine Learning 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. |
| title | A Stable and Efficient Covariate-Balancing Estimator for Causal Survival Effects |
| topic | Methodology Machine Learning |
| url | https://arxiv.org/abs/2310.02278 |