Faculty
Mert Demirer is the Ford Foundation International Career Development Assistant Professor and an Assistant Professor of Applied Economics at the MIT Sloan School of Management.
Demirer’s main area of research is industrial organization with a particular focus on developing new methods to analyze firm behavior, productivity, and market power. He also conducts research on machine learning for causal inference. This work investigates how to incorporate machine learning tools into econometrics to identify causal effects in economic research.
Prior to joining MIT Sloan, Demirer was a Postdoctoral Researcher at Microsoft Research. He holds a Master’s degree in economics from Koc University and a PhD in economics from MIT.
Featured Publication
"Double/Debiased Machine Learning for Treatment and Structural Parameters."Chernozhukov, Victor, Denis Chetveriko, Mert Demirer, Esther Duflo, Christian Hansen, Whitney Newey, and James Robins. The Econometrics Journal Vol. 21, No. 1 (2018): C1-C68. arXiv Preprint.
Mert Demirer, Vasilis Syrgkanis, Greg Lewis, and Victor Chernozhukov. In Proceedings of the Thirty-third Conference on Neural Information Processing Systems, Vancouver, BC: December 2019. Supplemental. Download Paper.
Demirer, Mert, Francis X. Diebold, Laura Liu, and Kamil Yilmaz. Journal of Applied Economics Vol. 33, No. 1 (2018): 1-15. NBER Preprint.
Chernozhukov, Victor, Denis Chetverikov, Mert Demirer, Esther Duflo, Christian Hansen, and Whitney Newey. American Economic Review Papers and Proceedings Vol. 107, No. 5 (2017): 261-265. arXiv Preprint.
Once again, AI was everywhere. But research about federal spending leads the list.
When software developers were given access to an AI coding tool, productivity increased — particularly among newer hires and more junior employees.
Research from Daron Acemoglu, Simon Johnson, Danielle Li, and Mert Demirer is referenced in this article about the effects of AI on workers.
