构建经济论文因果主张图谱，量化分析因果证据占比与影响力关系。
Causal Claims in Economics
- 将论文转化为标准化概念网络，标注因果/非因果关系
- 1990年因果关系占7.7%，2020年升至31.7%
- 因果叙事结构强的论文更易获高引，适合关注研究可信度者
随着经济学研究规模扩大，如何以可比、可聚合的方式表示论文主张成为关键瓶颈。本文提出证据标注的因果主张图谱，将每篇论文转化为由标准化经济概念（节点）和陈述关系（边）构成的有向网络，每条边标注证据来源，包括是否基于因果推断设计或非因果证据。通过结构化的多阶段AI工作流，我们为1980至2023年间44,852篇经济学论文构建了该图谱。结果显示，因果边比例从1990年的7.7%上升至2020年的31.7%。因果叙事结构与因果新颖性指标均与顶尖期刊发表及长期引用正相关，而非因果对应指标则关联较弱或呈负相关。
原文摘要 · Abstract (English)
As economics scales, a key bottleneck is representing what papers claim in a comparable, aggregable form. We introduce evidence-annotated claim graphs that map each paper into a directed network of standardized economic concepts (nodes) and stated relationships (edges), with each edge labeled by evidentiary basis, including whether it is supported by causal inference designs or by non-causal evidence. Using a structured multi-stage AI workflow, we construct claim graphs for 44,852 economics papers from 1980-2023. The share of causal edges rises from 7.7% in 1990 to 31.7% in 2020. Measures of causal narrative structure and causal novelty are positively associated with top-five publication and long-run citations, whereas non-causal counterparts are weakly related or negative.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。