Welcome to our Project!

• 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.

Our Mission

• Synthesize Causal Evidence from Economics (a global meta study) in Graphical format. See your causal graph here.

• 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.

Key Findings of our paper

• Full Working Paper Available at arXiv Non-technical VoxEU Summary of Paper and of Methods. 

Evolution of Empirical Methods

• 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

Causal Narrative Complexity Drives Publication and Citations

• 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.

Novelty and Filling Research Gaps: Good for Getting In, Not Guaranteed for Citations 

• 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 vs. Peripheral Concepts

• 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.

Balancing Multiple Causes and Fewer Outcomes

• 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.

Transparency and Replicability Concerns (not in paper)

• 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.

Our Approach

Leveraging a custom Artificial Intelligence (AI) pipeline, we process vast amounts of text to extract and structure causal relationships. Here's how we build the causal graph:

1. Data Collection: Gathering a comprehensive corpus of working papers from NBER and CEPR.

2. AI-Powered Extraction: Using our AI model to identify causal claims, empirical methods, and key economic concepts within each paper.

3. Standardization of Concepts: Mapping extracted variables to official Journal of Economic Literature (JEL) codes for consistency.

4. Construction of the Causal Graph: Connecting economic concepts through identified causal relationships to form a detailed causal graph.

5. Visualization: Creating graphical representations that illustrate how economic ideas are causally linked over time.

For the full research: Garg, P. & Fetzer, T. (2024), Causal Claims in Economics. Working Paper

Scroll down to explore some example graphs or find your own using our graph build tool!

These examples demonstrate how different research designs and topics result in varying causal graph structures. They showcase the utility of our approach in capturing and quantifying the complexity and novelty of economic research.

We build such causal graphs for all papers in economics in NBER and CEPR from 1980-2024. 

A Growing Project

This is just the beginning of our journey. Our project is continuously expanding, and we are committed to adding new features and insights to the website. Upcoming enhancements include:

• Interactive Causal Graphs: Explore the web of economic concepts and their causal linkages interactively.

• Expanded Data and Analysis: Incorporate more papers across disciplines and refine our AI methods for deeper insights.

• Collaborative Opportunities: Engage with the research community to enrich our understanding of economic causality.