267 citations · 328 across the 7 of their papers we have counts for
7 papers
Chaining Tasks, Redefining Work: A Theory of AI Automation
Mert Demirer, John J. Horton, Nicole Immorlica +2
We study the automation and productivity effects of AI when production is a sequence of interdependent steps with a Leontief structure. Each step can be executed manually by labor,…
Comment on "Sequential validation of treatment heterogeneity" and "Comment on generic machine learning inference on heterogeneous treatment effects in randomized experiments"
Victor Chernozhukov, Mert Demirer, Esther Duflo +1
We warmly thank Kosuke Imai, Michael Lingzhi Li, and Stefan Wager for their gracious and insightful comments. We are particularly encouraged that both pieces recognize the importan…
The Impact of AI on Developer Productivity: Evidence from GitHub Copilot
Sida Peng, Eirini Kalliamvakou, Peter Cihon +1
Generative AI tools hold promise to increase human productivity. This paper presents results from a controlled experiment with GitHub Copilot, an AI pair programmer. Recruited soft…
Semi-Parametric Efficient Policy Learning with Continuous Actions
Mert Demirer, Vasilis Syrgkanis, Greg Lewis +1
We consider off-policy evaluation and optimization with continuous action spaces. We focus on observational data where the data collection policy is unknown and needs to be estimat…
Fisher-Schultz Lecture: Generic Machine Learning Inference on Heterogenous Treatment Effects in Randomized Experiments, with an Application to Immunization in India
Victor Chernozhukov, Mert Demirer, Esther Duflo +1
We propose strategies to estimate and make inference on key features of heterogeneous effects in randomized experiments. These key features include best linear predictors of the ef…
Double/Debiased/Neyman Machine Learning of Treatment Effects
Victor Chernozhukov, Denis Chetverikov, Mert Demirer +3
Chernozhukov, Chetverikov, Demirer, Duflo, Hansen, and Newey (2016) provide a generic double/de-biased machine learning (DML) approach for obtaining valid inferential statements ab…
Double/Debiased Machine Learning for Treatment and Causal Parameters
Victor Chernozhukov, Denis Chetverikov, Mert Demirer +4
Most modern supervised statistical/machine learning (ML) methods are explicitly designed to solve prediction problems very well. Achieving this goal does not imply that these metho…