Vibe Econometrics and the Analysis Contract
“氛围计量经济学”与分析契约AI 解读 ↓
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“氛围计量经济学”与分析契约
- 研究问题
- 当人工智能降低因果分析的执行门槛，而识别有效性无法从输出本身判断时，如何治理错误更易传播且更具说服力的问题？
- 方法与思路
- 作者区分方法与数据不匹配、置信度包装和隐性分析分叉，并借鉴预分析计划与因果路线图提出因果声明前的分析契约。
- 材料陈述的贡献
- 摘要提出由方法与数据契约、数据审计及反证标准预承诺组成的框架，论证人工智能改变了既有推断失败的传播与可见性。
- 阅读关注点
- 应核对三类失败模式的定义和支撑证据、契约条款如何执行，以及预先约定的反证标准能否实际约束分析选择。
- 阅读边界
- 这是 arXiv 预印本，不能据此声称已获同行评审或接收；摘要提供概念性治理主张，未说明框架有效性的实证评估。
原文摘要
"Vibe coding" and "vibe analytics" have been framed as a democratization of technical capability. This paper argues that AI-assisted methodology more broadly, or what I call "vibe methodology," also democratizes the failure modes specific to each domain. When AI assists with methods whose validity depends on assumptions that cannot be verified from the output alone (a class I call "vibe inference"), the failure surface is structurally different: the output does not reliably signal invalidity, and when it does, recognizing the signal requires the expertise the workflow bypasses. I focus on "vibe econometrics," the subset of AI-assisted causal analysis where identification can be named faster than it can be audited. The claim of this paper is not that AI invents inferential failures that did not previously exist, but that it changes their incidence, observability, and persuasive force enough to create a practically distinct governance problem. This results in three failure modes: method-data mismatch, where AI bypasses expertise at execution; confidence laundering, where AI amplifies the credibility of formatted output; and invisible forking, which spans both. What is new is not the failure modes but AI's industrialization of their packaging. The barrier between naming a method and executing it has collapsed, and weak foundations, dressed as rigorous analysis, now reach audiences at a scale, speed, and polish that previously required expertise. I propose the Analysis Contract, a pre-commitment framework that adapts the logic of pre-analysis plans and the Causal Roadmap to the AI-assisted setting. The contract imposes three conditions before a causal claim is made: a method-data contract, a data audit, and a pre-commitment statement defining what would count as a disconfirming result. The framework generalizes across domains of vibe inference through domain-specific instantiation.