Empirical Methods in the Age of AI

Artificial intelligence is transforming how researchers collect, analyze, and learn from data. As AI systems become increasingly integrated into scientific discovery, business decision-making, and policy analysis, they are reshaping both the questions researchers can ask and the methods they use to answer them.

Hosted by the Stanford Causal Science Center and the Graduate School of Business, the Empirical Methods in the Age of AI Conference brings together leading academics and industry practitioners to explore the frontier of data-driven research in an era of powerful AI tools. The conference will examine how AI is changing empirical workflows, enabling new forms of measurement and analysis, and creating opportunities—and challenges—for generating reliable evidence from data.

Through keynote talks, panels, and discussions, participants will explore topics such as AI-assisted research, data analysis workflows, causal inference, econometrics, measurement, and the evolving relationship between human expertise and machine intelligence. The event is designed to foster dialogue between researchers developing new empirical methods and practitioners applying them to real-world problems.

Registration will open on this page 4-6 weeks before the event; please check back soon to purchase your ticket and learn more about the speakers and agenda.

Conference Pass Price

• General Admission*: $395
• Non-Stanford Academia, Nonprofit, Government* (academic, nonprofit, or government email required; contact datascience@stanford.edu for assistance): $195
• Stanford (stanford.edu email required): $30

* Includes complimentary parking

Confirmed Speakers

• Alberto Abadie, MIT
• Anastasios Angelopoulos, Arena
• Isaiah Andrews, MIT
• Susan Athey, Stanford
• David Blei, Columbia University
• Victor Chernozhukov, MIT
• Krzysztof Geras, NYU/Ataraxis AI
• Amir Goldberg, Stanford
• Paul Goldsmith-Pinkham, Yale
• Matteo Maggiori, Stanford
• Francesca Molinari, Cornell University
• Whitney Newey, MIT 
• Chris Nosko, Amazon
• Ashesh Rambachan, MIT 
• Yiqing Xu, Stanford