Econometrics with Pre-Trained Embeddings for Unstructured Data
使用预训练嵌入处理非结构化数据的计量经济学AI 解读 ↓
← 返回论文列表与筛选
阅读说明
本页整理公开书目信息，不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成，未完成全文核验，可能有误；具体假设、证明与结论请核对原文。摘要保留来源文本，未经模型改写。
DOI 首次登记不等于接收或发表日期。部分更正、评论等条目没有独立摘要；缺少可靠材料时保留待补状态。
本页为预印本版本；arXiv 收录、作者填写的期刊信息以及作者既有发表经历，都不单独证明本篇已接收。作者发表记录按 OpenAlex 身份与完整姓名、本站覆盖期刊 ISSN 及正式卷期核对，只展示已核实例，档案仍可能误合并。
引用量采用 OpenAlex 单条关联记录，不合计多个记录；关联记录可能包含正式发表版本，不等于此版本单独引用。缺失不视为零，引用指标不代表论文质量。
关注作者按书目中的完整姓名匹配，同名和姓名变体需人工辨别。相关论文先按共享细分方向和具体方法排列，再参考通用方法与发表日期。
分类依据与纠错
AI 根据所提供的论文材料归类：摘要明确涵盖非结构化混杂控制下的平均处理效应及需求模型价格弹性估计。
反馈分类问题
正在核对关联版本…
代码、数据与补充材料
正在核对对应版本的资源…
关注这篇论文的作者
使用预训练嵌入处理非结构化数据的计量经济学
- 研究问题
- 为其他数据和任务训练的文本或图像嵌入，何时能可靠用于经济分析，其识别与估计误差会如何影响目标参数？
- 方法与思路
- 作者提出包括可迁移性在内的充分条件，推导收敛率，并构造无需重新估计嵌入与干扰函数的自助检验。
- 材料陈述的贡献
- 摘要称理论适用于多类双重机器学习任务，包括因果效应、需求价格弹性和缺失数据处理，并展示劳动供给应用。
- 阅读关注点
- 应核对可迁移性的定义、嵌入识别问题的处理，以及自助检验条件和预训练误差进入目标估计的具体方式。
- 阅读边界
- 这是 arXiv 预印本，同行评审与期刊接收未核实；摘要未列出充分条件，不能据此认定任意预训练嵌入适合目标任务。
原文摘要
Unstructured data, such as images and text, are increasingly used in empirical economics. Since training machine-learning models on unstructured data is costly, economists often use off-the-shelf pre-trained deep learning models developed by computer scientists to extract embeddings, which are then used as covariates in target economic analyses. Despite the popularity of this practice, its theoretical foundations remain limited. There are two main difficulties. First, pre-trained models are typically trained on different datasets and for different tasks, making it unclear when they can be used reliably for the target task. Second, the embedding function is subject to an identification problem, complicating the analysis of its estimation error and the effect of that error on the target task. We provide sufficient conditions to overcome these difficulties. A key condition, which we call transferability, governs the convergence rate we derive. To assess transferability, we develop a computationally feasible bootstrap test that does not require re-estimating the embeddings and nuisance functions. Our theory applies to a wide range of double machine learning applications, including partially linear regression with unstructured controls, price elasticity estimation in demand models accounting for product quality measured by images and text, missing-data imputation using unstructured data, and average treatment effect estimation with unstructured confounders. As an empirical application, we estimate the labor supply elasticity on Amazon Mechanical Turk, an online labor market platform, using job-description embeddings as controls.