Vibe计量经济学与分析合同
Vibe Econometrics and the Analysis Contract
AI总结：
本文探讨了AI辅助方法在经济学中的应用，指出其改变了失败模式的出现、可见性和说服力，提出了分析合同框架以应对新的治理问题。
摘要：
"Vibe coding"和"vibe analytics"被框定为技术能力的民主化。本文认为，更广泛的AI辅助方法，或我称为"vibe方法论"，也民主化了每个领域的特定失败模式。当AI协助的方法有效性依赖于无法仅从输出验证的假设（我称之为"vibe推断"）时，失败表面在结构上不同：输出不可靠地信号无效性，且当它确实发生时，识别信号需要被绕过的专业知识。我专注于"vibe计量经济学"，即AI辅助因果分析中识别速度超过审计速度的子集。本文的主张不是AI发明了以前不存在的推断失败，而是它改变了这些失败的出现、可见性和说服力，足以产生实际不同的治理问题。这导致三种失败模式：方法-数据不匹配，其中AI在执行时绕过了专业知识；信心洗白，其中AI放大了格式化输出的可信度；以及看不见的分支，涵盖两者。新的是不是失败模式而是AI对其包装的工业化。命名方法和执行之间的障碍已消失，薄弱的基础，伪装成严谨的分析，现在能够以规模、速度和精致度触及以前需要专业知识的受众。我提出分析合同，一个适应预分析计划和因果路线图逻辑的AI辅助设置框架。该合同在做出因果声明前施加三个条件：方法-数据合同、数据审计和预承诺声明，定义什么会被视为否定结果。该框架通过领域特定的实例泛化到各种vibe推断领域。
英文摘要：
"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.
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