Empirical Methods and AI¶
The intersection of AI and empirical methods raises important methodological questions about how AI tools affect the rigor and validity of empirical research in economics.
Context & Background¶
AI makes it dramatically easier to run many specifications, test numerous hypotheses, and explore data in ways that raise concerns about:
- P-hacking acceleration: AI can quickly generate hundreds of regression specifications, making it tempting to search for significant results
- Specification searching: Automated exploration of control variables, sample restrictions, and functional forms
- HARKing: Hypothesizing After Results are Known becomes easier when AI can rapidly generate post-hoc explanations
- Data mining: AI-assisted pattern finding without theoretical grounding
At the same time, AI can improve empirical methods through better data cleaning, more comprehensive robustness checks, and automated replication verification.
Practical Implications¶
- Pre-register analyses: Commit to specifications before running them, especially when using AI
- Document all specifications: Record every analysis the AI ran, not just the ones you report
- Use AI for robustness: After finding your main result, use AI to thoroughly stress-test it
- Maintain theoretical discipline: Let theory guide hypotheses, not AI-discovered correlations
- Transparency about AI involvement: Report how AI was used in the empirical analysis