English(EN) When Predictions Become Regressors: A Split-Sample Correction for Biases in Downstream Inference

新方法校正大型语言模型驱动的回归分析中的偏差

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-05 04:00

研究人员开发了一种新颖的方法，用于校正在使用预测生成的度量（例如来自大型语言模型（LLM）的度量）作为回归分析中的解释变量时产生的偏差。这种样本分割工具变量方法使用在独立样本分割上创建的多个度量来构建工具变量，从而减轻估计偏差。所提出的技术在理论上是可靠的，易于实现，并且不需要额外的数据，这已通过模拟以及在德国议会立法结果和中国政治风险中的应用得到证明。 AI

影响为在下游分析中使用LLM输出提供了统计校正，提高了研究结果的可靠性。

排序理由提出新统计方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv stat.ML 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

本文如何被排名

Signal score: 0 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 提出新统计方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
Source corroboration: Single-source cluster; Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics: paper, other; Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
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Story freshness: 24 days old; Aged out of breaking-news scoring windows; ranking reflects the durable signal from the full source set.

完整方法见我们的编辑标准。

报道来源 [1]

arXiv stat.ML TIER_1 English(EN) · Nathan Canen, Ted Enamorado · 2026-08-05 04:00

当预测成为回归器：下游推理偏差的分样本校正

arXiv:2608.02909v1 Announce Type: cross Abstract: Prediction-based methods, including Large Language Models (LLMs) and other machine learning techniques, are often used to construct measures of political phenomena that are difficult to quantify directly, such as policy positions …

本文如何被排名

报道来源 [1]

当预测成为回归器：下游推理偏差的分样本校正

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