研究人员引入了一个名为预测驱动因果推断（PPCI）的新框架，以改进因果和结构参数的估计。该方法利用未标记的辅助回归量以及标记数据，与仅使用标记观测值的方法相比，实现了更小的渐近方差。提出的DML-PPCI方法，包括EE-DML-PPCI和TMLE-DML-PPCI，旨在匹配导出的效率边界并利用Neyman正交得分进行估计。 AI
排序理由 该集群包含一篇详细介绍新统计方法的学术论文。
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新的PPCI框架通过辅助数据增强因果推断
报道来源 [2]
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Prediction-Powered Causal Inference by Automatic Debiased Machine Learning and Semi-Supervised Riesz Regression
arXiv:2606.12892v1 Announce Type: new Abstract: This study investigates semiparametric efficient estimation of causal and structural parameters in a semi-supervised setting. In our setting, unlabeled auxiliary regressors are available in addition to labeled observations consistin…
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Prediction-Powered Causal Inference by Automatic Debiased Machine Learning and Semi-Supervised Riesz Regression
This study investigates semiparametric efficient estimation of causal and structural parameters in a semi-supervised setting. In our setting, unlabeled auxiliary regressors are available in addition to labeled observations consisting of outcomes and regressors. Our goal is to con…