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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.

2024

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

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.  

Contact
Information

Technology & Operations Management, 
Harvard Business School

Morgan Hall, Soldiers Field
Boston, MA 02163

mili at hbs dot edu (Academic)

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©2023 by Michael Lingzhi Li

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