Causal Claims in Economics
Prashant Garg, Thiemo Fetzer
TL;DR
This paper develops a knowledge-graph framework to map economic claims across 44,000+ NBER/CEPR working papers, distinguishing claims supported by causal inference methods from general associations. It uses an LLM-driven pipeline to extract structured sources, sinks, and causal edges, mapping variables to JEL codes and linking to publication and citation data. The core findings show a substantial rise in causal claims from 1990 to 2020, and that causal narrative complexity and novelty robustly predict top-tier journal acceptance and long-run citations, while engagement with central, well-established concepts better explains long-run impact. The study highlights a tension between editorial emphasis on causal rigor and novelty and the broader academic audience's attachment to central topics, offering actionable guidance on balancing methodological innovation with conceptual integration.
Abstract
We analyze over 44,000 NBER and CEPR working papers from 1980 to 2023 using a custom language model to construct knowledge graphs that map economic concepts and their relationships. We distinguish between general claims and those documented via causal inference methods (e.g., DiD, IV, RDD, RCTs). We document a substantial rise in the share of causal claims-from roughly 4% in 1990 to nearly 28% in 2020-reflecting the growing influence of the "credibility revolution." We find that causal narrative complexity (e.g., the depth of causal chains) strongly predicts both publication in top-5 journals and higher citation counts, whereas non-causal complexity tends to be uncorrelated or negatively associated with these outcomes. Novelty is also pivotal for top-5 publication, but only when grounded in credible causal methods: introducing genuinely new causal edges or paths markedly increases both the likelihood of acceptance at leading outlets and long-run citations, while non-causal novelty exhibits weak or even negative effects. Papers engaging with central, widely recognized concepts tend to attract more citations, highlighting a divergence between factors driving publication success and long-term academic impact. Finally, bridging underexplored concept pairs is rewarded primarily when grounded in causal methods, yet such gap filling exhibits no consistent link with future citations. Overall, our findings suggest that methodological rigor and causal innovation are key drivers of academic recognition, but sustained impact may require balancing novel contributions with conceptual integration into established economic discourse.