这篇论文深入探讨了AI在经济学测量中的应用，提供了关于如何进行可信推理的宝贵见解，对于对AI在测量领域应用感兴趣的人来说是个好资源。
AI模型将非结构化数据转换为结构化变量，推动经济学测量变革。本文提供指导，描述AI在测量流程中的三个阶段：发现、构建定义和观察，并强调对AI生成变量的可信推理需要适当设计的验证。同时，探讨如何支持有效推理，即使AI预测存在任意偏差，以及随机验证样本不可用时如何处理。
The Measurement Revolution? Credible Measurement and Inference in the Age of AI
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.