English(EN) Reliable Selection of Heterogeneous Treatment Effect Estimators

新方法可靠地选择异质性处理效应估计器

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-04 04:00

研究人员开发了一种新颖的程序，用于选择最有效的异质性处理效应 (HTE) 估计器，特别是在无法直接观察到真实处理效应的情况下。该方法将选择过程构建为一个多重检验问题，并利用交叉拟合的指数加权检验统计量。一项关键创新是双向样本分割技术，它将干扰项估计与权重学习分开，确保准确推断的稳定性并提供可靠的误差控制。 AI

影响这项研究提供了一种更可靠的方法来选择用于估计处理效应的最佳机器学习模型，有可能改善依赖因果推断的领域的决策。

排序理由该集群包含一篇详细介绍机器学习新统计方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv stat.ML TIER_1 English(EN) · Jiayi Guo, Zijun Gao · 2026-09-04 04:00

异质处理效应估计量的可靠选择

arXiv:2511.18464v2 Announce Type: replace Abstract: We study the problem of selecting the best heterogeneous treatment effect (HTE) estimator from a collection of candidates in settings where the treatment effect is fundamentally unobserved. We cast estimator selection as a multi…

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异质处理效应估计量的可靠选择

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