English(EN) Identification and Bounding of Central Moments of Causal Effects Using Marginal Moments Information

新方法使用边际数据界定因果效应矩

作者 PulseAugur 编辑部 · [2 个来源] · 2026-07-06 11:40

研究人员开发了一种新方法，仅利用潜在结果的边际中心矩来识别和界定个体因果效应（ICE）的中心矩。与需要了解潜在结果完整边际分布的现有方法相比，这种方法在实证应用中更具实用性。该论文通过两个案例研究展示了该方法的实用性。 AI

排序理由该条目是一篇详细介绍新统计学方法的学术论文。[lever_c_demoted from research: ic=1 ai=0.4]

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报道来源 [2]

arXiv stat.ML TIER_1 English(EN) · Naoya Hashimoto, Yuta Kawakami, Jin Tian · 2026-07-07 04:00

利用边际矩信息识别和界定因果效应的中心矩

arXiv:2607.04957v1 Announce Type: cross Abstract: Evaluating the causal effect of a treatment on an outcome is a central objective in causal inference. While the average causal effect summarizes the mean impact of treatment, the central moments of the individual causal effect (IC…
arXiv stat.ML TIER_1 English(EN) · Jin Tian · 2026-07-06 11:40

使用边际矩信息识别和界定因果效应的中心矩

Evaluating the causal effect of a treatment on an outcome is a central objective in causal inference. While the average causal effect summarizes the mean impact of treatment, the central moments of the individual causal effect (ICE) characterize the shape of the ICE distribution,…