Mert Demirer

Faculty

Mert Demirer

Support Staff

Get in Touch

Title

About

Academic Groups

Academic Area

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.

 

Publications

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.

"Do Mergers and Acquisitions Improve Efficiency? Evidence from Power Plants."

Demirer, Mert and Ömer Karaduman. Journal of Political Economy. Forthcoming. Download Preprint.

"The Effects of Generative AI on High-Skilled Work: Evidence from Three Field Experiments with Software Developers."

Cui, Kevin Zheyuan, Mert Demirer, Sonia Jaffe, Leon Musolff, Sida Peng, and Tobias Salz. Management Science. Forthcoming. Download Preprint.

"Frontiers: The Intended and Unintended Consequences of Privacy Regulation for Consumer Marketing."

Dubé, Jean-Pierre, John G. Lynch, Dirk Bergemann, Mert Demirer, Avi Goldfarb, Garrett Johnson, Anja Lambrecht, Tesary Lin, Anna Tuchman, and Catherine Tucker. Marketing Science Vol. 44, No. 5 (2025): 975-984.

"Fisher–Schultz Lecture: Generic Machine Learning Inference on Heterogeneous Treatment Effects in Randomized Experiments, With an Application to Immunization in India."

Chernozhukov, Victor, Mert Demirer, Esther Duflo, Iván Fernández-Val. Econometrica Vol. 93, No. 4 (2025): 1121-1164.

"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.

Load More

Recent Insights

Ideas Made to Matter

The top 10 MIT Sloan articles of 2024

Once again, AI was everywhere. But research about federal spending leads the list.

Dec 3, 2024
Read Article
Ideas Made to Matter

How generative AI affects highly skilled workers

When software developers were given access to an AI coding tool, productivity increased — particularly among newer hires and more junior employees.

Nov 4, 2024
Read Article
Load More

Media Highlights

Press The New York Times

Mass layoffs are scary, but probably not a sign of the A.I. apocalypse

Experts say the transition to an A.I.-powered workplace is likely to be more gradual, in many cases occurring as new companies, built to exploit A.I., take market share from more established companies that are slower to embrace it. "Widespread adoption is going to happen at the new firms," said assistant professor Mert Demirer. "It's always the case that the smaller the production process, the more the process is easier to change."

Read Article
Press The New York Times

Has the decline of knowledge work begun?

A paper co-authored by assistant professor Mert Demirer found that software developers who used an A.I. coding assistant improved a key measure of productivity by more than 25 percent. Demirer said that a software developer's job could change over the longer term, so that the human coder would become a kind of project manager overseeing multiple A.I. assistants.

Read Article