标题： 语言模型能否在没有统计偏差的情况下增强随机实验的功效？ 显示英文标题

标题： Can language models boost the power of randomized experiments without statistical bias?

摘要： 随机实验或随机对照试验（RCTs）是因果推断的黄金标准，但成本和样本量限制了其效力。同时，现代RCTs通常收集丰富且非结构化的数据，这些数据对结果具有高度预测性，但在因果分析中很少被使用。我们引入了CALM（Causal Analysis leveraging Language Models），这是一种统计框架，将大型语言模型（LLMs）的预测与已建立的因果估计器相结合，以提高精度同时保持统计有效性。CALM将LLM输出视为辅助预后信息，并通过异质校准步骤纠正其潜在偏差，该步骤对预测进行残差化并最优加权。我们证明，即使LLM预测存在偏差，CALM仍然是一致的，并且在各种因果效应上比增强逆概率加权估计器具有效率优势。特别是，CALM开发了一个少样本变体，该变体聚合来自随机采样的演示集的预测。所产生的U统计量类似预测器恢复了独立同分布结构，并且也减轻了提示选择的变异性。实证研究表明，在校准到移动应用抑郁症RCT的模拟中，CALM相对于其他基准方法具有更低的方差，在零样本和少样本设置中有效，并且在不同提示设计下保持稳定。通过有原则地利用LLMs来利用非结构化数据和预训练期间学到的外部知识，CALM为RCT中的更精确因果分析提供了一条实用路径。 显示英文摘要

摘要： Randomized experiments or randomized controlled trials (RCTs) are gold standards for causal inference, yet cost and sample-size constraints limit power. Meanwhile, modern RCTs routinely collect rich, unstructured data that are highly prognostic of outcomes but rarely used in causal analyses. We introduce CALM (Causal Analysis leveraging Language Models), a statistical framework that integrates large language models (LLMs) predictions with established causal estimators to increase precision while preserving statistical validity. CALM treats LLM outputs as auxiliary prognostic information and corrects their potential bias via a heterogeneous calibration step that residualizes and optimally reweights predictions. We prove that CALM remains consistent even when LLM predictions are biased and achieves efficiency gains over augmented inverse probability weighting estimators for various causal effects. In particular, CALM develops a few-shot variant that aggregates predictions across randomly sampled demonstration sets. The resulting U-statistic-like predictor restores i.i.d. structure and also mitigates prompt-selection variability. Empirically, in simulations calibrated to a mobile-app depression RCT, CALM delivers lower variance relative to other benchmarking methods, is effective in zero- and few-shot settings, and remains stable across prompt designs. By principled use of LLMs to harness unstructured data and external knowledge learned during pretraining, CALM provides a practical path to more precise causal analyses in RCTs.