Mert Demirer


Mert Demirer

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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.



"Semi-Parametric Efficient Policy Learning with Continuous Actions."

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.

"Estimating Global Bank Network Connectedness."

Demirer, Mert, Francis X. Diebold, Laura Liu, and Kamil Yilmaz. Journal of Applied Economics Vol. 33, No. 1 (2018): 1-15. NBER Preprint.

"Double/Debiased/Neyman Machine Learning of Treatment Effects."

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.

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