Causal Claims in Economics

TL;DR

This paper introduces a method to represent economic research claims as evidence-annotated graphs, enabling analysis of causal inference trends and their impact on publication success.

Contribution

It presents a novel AI-driven approach to map economic papers into standardized claim graphs with causal evidence annotations, covering nearly 45,000 papers from 1980 to 2023.

Findings

Causal edges in economic papers increased from 7.7% in 1990 to 31.7% in 2020.

Higher causal narrative and novelty correlate with top-tier publications and citations.

Non-causal evidence is weakly or negatively associated with publication success.

Abstract

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.