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Safe Inference for
Machine Learning

How can we make sure a machine learning algorithm is doing what we want it to do, at the performance of what we expect? We develop new methodologies for rigorous and safe evaluation of machine learning under both randomized and observational settings with minimal assumptions.


Statistical Inference for Heterogeneous Treatment Effects in Randomized Experiments (with Imai, K.). Revision at Journal of Business and Economic Statistics.


Experimental Evaluation of Individualized Treatment Rules (with Imai, K.). Journal of the American Statistical Association (2021): 1-41. 


Pricing for Heterogeneous Products: Analytics for Ticket Reselling (with Alley, M., Biggs, M., Hariss, R., Herrmann, C., & Perakis, G.). Manufacturing & Service Operations Management (2022). Ahead of Print.  

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