AI让经济测量低成本规模化，但需严格验证才能可信
The Measurement Revolution? Credible Measurement and Inference in the Age of AI
- AI在发现、定义、观测三阶段介入测量流程
- 必须用显式标准验证AI生成变量，避免主观判断
- 即使预测有偏，合理验证样本仍可支撑有效推断
人工智能正改变经济学的测量方式。AI模型能以低成本将文本、图像等非结构化数据转化为结构化变量，使以往难以实现的大规模测量变得可行。这导致测量瓶颈从‘找到可扩展指标’转变为‘在多个合理选项中选择’，而不同选择可能导致不同实证结论。本文梳理了AI在测量流程中的三个阶段——发现、构念定义与观测——及其对研究者的要求。强调使用AI生成变量进行可信推断，需设计恰当的验证机制：将测量锚定于明确标准，而非仅凭‘代理变量合理’的模糊主张。进一步分析了验证样本如何支持有效推断，即便AI预测存在任意偏差；并探讨当随机验证样本不可得时的应对策略。
原文摘要 · Abstract (English)
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.
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