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.
2024
Statistical Inference for Heterogeneous Treatment Effects in Randomized Experiments (with Imai, K.). Accepted at Journal of Business and Economic Statistics.
2023
Statistical Performance Guarantee for Subgroup Identification with Generic Machine Learning (with Imai, K.). Submitted to Biometrika.
2023
Experimental Evaluation of Individualized Treatment Rules (with Imai, K.). Journal of the American Statistical Association (2021): 1-41.
2019
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.