The Measurement Revolution? Credible Measurement and Inference in the Age of AI

2026-08-24

AI Summary

本文探讨了AI在经济测量中的应用，通过将非结构化数据转化为结构化变量来解决测量难题，并强调了通过适当验证确保AI生成变量的可信度。

Abstract

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.

Problem

• Artificial Intelligence
• Measurement
• Inference
• Validation
• Economics

Innovation

• Artificial Intelligence
• Unstructured Data
• Validation Samples
• Economic Measurement
• Credible Inference