测量革命？AI时代的可信测量与推断
The Measurement Revolution? Credible Measurement and Inference in the Age of AI
- National Bureau of Economic Research（美国国家经济研究局）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本综述探讨AI对经济学测量的变革，明确AI进入测量流程的三个阶段，提出用AI生成变量需锚定明确标准的验证方法，还分析了偏差处理与无随机验证样本时的应对方案。
AI中文摘要：
人工智能（AI）正在改变经济学中的测量方式。AI模型能以低成本将文本、图像等非结构化数据转换为结构化变量，使此前难以规模化开展的测量成为可能。这使得瓶颈从寻找某一现象的可规模化测量指标，转向在众多看似合理的指标中进行选择，而这些指标可能会支持不同的实证结论。本综述为应对这一转变提供指导，描述了AI进入测量流程的三个阶段——发现、构念定义和观测，以及每个阶段对研究者的要求。我们认为，用AI生成的变量进行可信推断需要精心设计的验证：将测量锚定到明确标准，而非“代理变量合理”这类非正式说法。随后，我们探讨了当AI预测存在任意偏差时，验证样本如何支持有效推断，以及当无法获取随机验证样本时可采取的措施。
英文摘要：
Artificial intelligence (AI) is transforming measurement in economics. AI models convert unstructured data, such as text and images, into structured variables at low cost, making previously prohibitive measurement feasible at scale. This shifts the bottleneck from finding any scalable measure of a phenomenon to choosing among many plausible ones, which may support different empirical conclusions. This review provides guidance for navigating that shift. We describe three stages at which AI enters the measurement pipeline---discovery, construct definition, and observation---and what each demands of researchers. We argue that credible inference with AI-generated variables requires appropriately designed validation: anchoring measurement to explicit criteria, rather than informal claims that a proxy is reasonable. We then examine how validation samples support valid inference even when AI predictions are arbitrarily biased, and what can be done when a random validation sample is unavailable.
