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English(EN) When Sample-Splitting Choices Matter in TMLE and AIPW: Cross-Fitting, Targeting, and Repetition

研究论文分析样本拆分对 TMLE 和 AIPW 估计量的影响

作者 PulseAugur 编辑部 · [1 个来源] ·

一篇新的研究论文探讨了在机器学习领域使用 nuisance 模型时，样本拆分选择对 Targeted Maximum Likelihood Estimation (TMLE) 和 Augmented Inverse-Probability Weighting (AIPW) 方法性能的影响。研究发现，对 nuisance 模型进行交叉拟合显著提高了覆盖率，尤其是在重叠度低或接近正值的情况下。研究还强调了 TMLE 和 AIPW 估计量在性能上的差异，在某些条件下 AIPW 通常表现出更好的覆盖率，并指出重复拆分可以减少分区依赖性，但可能导致更保守的区间。 AI

排序理由 该集群包含一篇详细介绍统计学方法的论文。[lever_c_demoted from research: ic=1 ai=0.4]

在 arXiv stat.ML 阅读 →

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研究论文分析样本拆分对 TMLE 和 AIPW 估计量的影响

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该集群包含一篇详细介绍统计学方法的论文。[lever_c_demoted from research: ic=1 ai=0.4]
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报道来源 [1]

  1. arXiv stat.ML TIER_1 English(EN) · M. Ehsan Karim, Rainie Fu ·

    当样本分割选择在TMLE和AIPW中至关重要时：交叉拟合、目标化和重复

    arXiv:2610.16192v1 Announce Type: cross Abstract: Variants of targeted maximum likelihood estimation (TMLE) and augmented inverse-probability weighting (AIPW) with machine-learning nuisance models differ in which observations train them, how bias is corrected, and whether splits …

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