View Project Website: www.causal.claims.
Economics is a dynamic field that has witnessed a profound transformation over the past four decades.
The discipline has shifted towards establishing causal relationships using advanced empirical methods—a movement known as the "credibility revolution."
At the heart of our project is the creation of the Causal Graph of Economics.
We have analyzed over 44,000 working papers from the National Bureau of Economic Research (NBER) and the Centre for Economic Policy Research (CEPR) using AI to map out the intricate network of causal claims that shape economic research.
Explore structured and interconnected causal information extracted from economics research papers. Use the search bar to locate specific papers or authors. See your causal graph here.
This knowledge graph allows you to explore causal relationships in economic research:
Paper-Level Graph: Shows causal relationships extracted directly from individual papers, with interconnected nodes representing causes and effects.
Author-Level Graph: View an author’s research across years, with dropdowns to select the year-specific causal graphs.
Make complex economic research accessible to scholars, students, and enthusiasts alike. Check out CClaRA- Causal Claims Research Assistant, fine-tuned on the causal knowledge graph from economics papers.
Welcome to the Causal Claims Research Assistant (CClaRA) Built on Causal Graph of Economics.
A literature review tool grounded on the knowledge graph.
Search for papers by causal claims (where X causes Y)
Search for paper by concept nodes (e.g., effects of X, causes of X)
Search by claims made in specific journal (e.g. the top five)
Search by authors working on specific fields, and so on.
Joint with Thiemo Fetzer. View Draft Paper.
Coverage: VoxEU (Paper), VoxEU (Methods), Marginal Revolution
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.
Significant Increase in Causal Claims: The average proportion of causal claims in papers rose significantly from approximately 5% in 1990 to around 28% in 2020, reflecting the impact of the credibility revolution in economics.
Growth in Causal Inference Methods and decline in Theoretical and Simulation Methods
Depth Matters: Papers that develop deep, well-structured causal arguments (e.g., multiple causal paths or longer causal chains) significantly boost both top-journal acceptance (especially top five outlets) and subsequent citation counts.
Non-Causal Complexity Offers Little Reward: Simply adding more correlational or theoretical relationships (i.e., non-causal edges) does not show the same positive effect—and can even correlate negatively with citation impact.
Novel Causal Relationships: Introducing genuinely new causal edges or paths increases the likelihood of publication in elite journals. However, novelty alone does not guarantee higher long-term citations.
Gap Filling: Connecting underexplored topic pairs (i.e., bridging conceptual “gaps”) helps secure top-tier acceptance when backed by credible causal evidence. Once published, though, neither causal nor non-causal gap filling robustly predicts citation counts.
Central Topics Accumulate Citations: Engagement with well-established, high-visibility nodes (e.g. wage inequality, education/health) correlates strongly with more citations over time.
Top Journals Tend To Publish Frontier Areas: By contrast, top five outlets are more likely to accept papers exploring less central or specialized concepts—provided they use strong identification strategies—reflecting a taste for novelty or underexplored territory.
High Source-to-Sink Ratio: Within the causal subgraph, papers that present multiple causal factors converging on fewer key outcomes perform well in top-tier placements and citations.
Non-Causal Inversion: For non-causal relationships, having fewer causes and many diverse outcomes sometimes shows a modest positive association with top-journal publication—but has no similar payoff in citations.
Decline in Reporting Null Results: Reporting of null results declined from 15% in 1980 to around 8.6% in 2023, possibly reflecting increased pressure to produce significant findings and contributing to publication bias.
Increase in Use of Private Data: The use of private data doubled from about 4% in 1980 to above 8% in 2023, raising concerns about data accessibility, replicability, and transparency in economic research.