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.

翻译：随着经济学研究规模的扩大，一个关键瓶颈在于如何以可比较、可聚合的形式呈现论文所主张的观点。我们引入了证据标注的主张图，将每篇论文映射为一个由标准化经济学概念（节点）与陈述关系（边）构成的有向网络，其中每条边均根据证据基础进行标注，包括其是否得到因果推断设计或非因果证据的支持。通过一个结构化的多阶段人工智能工作流程，我们为1980年至2023年间的44,852篇经济学论文构建了主张图。因果边的占比从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的度量指标与顶级期刊（前五）发表及长期引用呈正相关，而非因果的对应指标则关联较弱或呈负相关。