Skip to Main Content
HBS Home
  • About
  • Academic Programs
  • Alumni
  • Faculty & Research
  • Baker Library
  • Giving
  • Harvard Business Review
  • Initiatives
  • News
  • Recruit
  • Map / Directions
Faculty & Research
  • Faculty
  • Research
  • Featured Topics
  • Academic Units
  • …→
  • Harvard Business School→
  • Faculty & Research→
  • Research
    • Research
    • Publications
    • Global Research Centers
    • Case Development
    • Initiatives & Projects
    • Research Services
    • Seminars & Conferences
    →
  • Publications→

Publications

Publications

Filter Results: (22) Arrow Down
Filter Results: (22) Arrow Down Arrow Up

Show Results For

  • All HBS Web  (119,654)
    • Faculty Publications  (22)

    Show Results For

    • All HBS Web  (119,654)
      • Faculty Publications  (22)

      Li, Michael LingzhiRemove Li, Michael Lingzhi →

      Page 1 of 22 Results →
      • 2025
      • Article

      Statistical Inference for Heterogeneous Treatment Effects Discovered by Generic Machine Learning in Randomized Experiments

      By: Kosuke Imai and Michael Lingzhi Li
      Researchers are increasingly turning to machine learning (ML) algorithms to investigate causal heterogeneity in randomized experiments. Despite their promise, ML algorithms may fail to accurately ascertain heterogeneous treatment effects under practical settings with... View Details
      Keywords: AI and Machine Learning; Mathematical Methods; Analytics and Data Science
      Citation
      Find at Harvard
      Read Now
      Related
      Imai, Kosuke, and Michael Lingzhi Li. "Statistical Inference for Heterogeneous Treatment Effects Discovered by Generic Machine Learning in Randomized Experiments." Journal of Business & Economic Statistics 43, no. 1 (2025): 256–268.
      • 2024
      • Article

      Neyman Meets Causal Machine Learning: Experimental Evaluation of Individualized Treatment Rules

      By: Michael Lingzhi Li and Kosuke Imai
      A century ago, Neyman showed how to evaluate the efficacy of treatment using a randomized experiment under a minimal set of assumptions. This classical repeated sampling framework serves as a basis of routine experimental analyses conducted by today’s scientists across... View Details
      Keywords: AI and Machine Learning; Research
      Citation
      Read Now
      Related
      Li, Michael Lingzhi, and Kosuke Imai. "Neyman Meets Causal Machine Learning: Experimental Evaluation of Individualized Treatment Rules." Journal of Causal Inference 12, no. 1 (2024).
      • July 2024
      • Supplement

      Hospital for Special Surgery: Returning to a New Normal? (B)

      By: Robert S. Huckman, Michael Lingzhi Li and Camille Gregory
      Citation
      Purchase
      Related
      Huckman, Robert S., Michael Lingzhi Li, and Camille Gregory. "Hospital for Special Surgery: Returning to a New Normal? (B)." Harvard Business School Supplement 624-093, July 2024.
      • 2024
      • Working Paper

      Learning to Cover: Online Learning and Optimization with Irreversible Decisions

      By: Alexander Jacquillat and Michael Lingzhi Li
      Keywords: Buildings and Facilities; AI and Machine Learning; Geographic Location; Strategic Planning
      Citation
      Read Now
      Related
      Jacquillat, Alexander, and Michael Lingzhi Li. "Learning to Cover: Online Learning and Optimization with Irreversible Decisions." Working Paper, June 2024.
      • June 2024 (Revised August 2024)
      • Case

      Hospital for Special Surgery: Returning to a New Normal? (A)

      By: Robert S. Huckman, Michael Lingzhi Li and Camille Gregory
      Early on the morning of April 27, 2020, Justin Oppenheimer stood outside the entrance to the lobby of the Hospital for Special Surgery (HSS) Pavilion Building with mixed emotions. On one hand, Oppenheimer, HSS’ Enterprise Chief Operating Officer and Chief Strategy... View Details
      Keywords: Operations Management; Scheduling; Optimization; COVID-19; Health Care and Treatment; Operations; Customer Focus and Relationships; Disruption; Health Industry; United States
      Citation
      Educators
      Purchase
      Related
      Huckman, Robert S., Michael Lingzhi Li, and Camille Gregory. "Hospital for Special Surgery: Returning to a New Normal? (A)." Harvard Business School Case 624-092, June 2024. (Revised August 2024.)
      • April 2024
      • Article

      A Machine Learning Algorithm Predicting Risk of Dilating VUR among Infants with Hydronephrosis Using UTD Classification

      By: Hsin-Hsiao Scott Wang, Michael Lingzhi Li, Dylan Cahill, John Panagides, Tanya Logvinenko, Jeanne Chow and Caleb Nelson
      Backgrounds: Urinary Tract Dilation (UTD) classification has been designed to be a more objective grading system to evaluate antenatal and post-natal UTD. Due to unclear association between UTD classifications to specific anomalies such as vesico-ureteral reflux (VUR),... View Details
      Keywords: Health Disorders; Health Testing and Trials; AI and Machine Learning; Health Industry
      Citation
      Read Now
      Related
      Wang, Hsin-Hsiao Scott, Michael Lingzhi Li, Dylan Cahill, John Panagides, Tanya Logvinenko, Jeanne Chow, and Caleb Nelson. "A Machine Learning Algorithm Predicting Risk of Dilating VUR among Infants with Hydronephrosis Using UTD Classification." Journal of Pediatric Urology 20, no. 2 (April 2024): 271–278.
      • 2024
      • Working Paper

      The Cram Method for Efficient Simultaneous Learning and Evaluation

      By: Zeyang Jia, Kosuke Imai and Michael Lingzhi Li
      We introduce the "cram" method, a general and efficient approach to simultaneous learning and evaluation using a generic machine learning (ML) algorithm. In a single pass of batched data, the proposed method repeatedly trains an ML algorithm and tests its empirical... View Details
      Keywords: AI and Machine Learning
      Citation
      Read Now
      Related
      Jia, Zeyang, Kosuke Imai, and Michael Lingzhi Li. "The Cram Method for Efficient Simultaneous Learning and Evaluation." Working Paper, March 2024.
      • 2023
      • Article

      Benchmarking Large Language Models on CMExam—A Comprehensive Chinese Medical Exam Dataset

      By: Junling Liu, Peilin Zhou, Yining Hua, Dading Chong, Zhongyu Tian, Andrew Liu, Helin Wang, Chenyu You, Zhenhua Guo, Lei Zhu and Michael Lingzhi Li
      Recent advancements in large language models (LLMs) have transformed the field of question answering (QA). However, evaluating LLMs in the medical field is challenging due to the lack of standardized and comprehensive datasets. To address this gap, we introduce CMExam,... View Details
      Keywords: Large Language Model; AI and Machine Learning; Analytics and Data Science; Health Industry
      Citation
      Read Now
      Related
      Liu, Junling, Peilin Zhou, Yining Hua, Dading Chong, Zhongyu Tian, Andrew Liu, Helin Wang, Chenyu You, Zhenhua Guo, Lei Zhu, and Michael Lingzhi Li. "Benchmarking Large Language Models on CMExam—A Comprehensive Chinese Medical Exam Dataset." Conference on Neural Information Processing Systems (NeurIPS), Datasets and Benchmarks Track 36 (2023).
      • September–October 2023
      • Article

      Interpretable Matrix Completion: A Discrete Optimization Approach

      By: Dimitris Bertsimas and Michael Lingzhi Li
      We consider the problem of matrix completion on an n × m matrix. We introduce the problem of interpretable matrix completion that aims to provide meaningful insights for the low-rank matrix using side information. We show that the problem can be... View Details
      Keywords: Mathematical Methods
      Citation
      Find at Harvard
      Read Now
      Purchase
      Related
      Bertsimas, Dimitris, and Michael Lingzhi Li. "Interpretable Matrix Completion: A Discrete Optimization Approach." INFORMS Journal on Computing 35, no. 5 (September–October 2023): 952–965.
      • March–April 2023
      • Article

      Pricing for Heterogeneous Products: Analytics for Ticket Reselling

      By: Michael Alley, Max Biggs, Rim Hariss, Charles Herrmann, Michael Lingzhi Li and Georgia Perakis
      Problem definition: We present a data-driven study of the secondary ticket market. In particular, we are primarily concerned with accurately estimating price sensitivity for listed tickets. In this setting, there are many issues including endogeneity, heterogeneity in... View Details
      Keywords: Price; Demand and Consumers; AI and Machine Learning; Investment Return; Entertainment and Recreation Industry; Sports Industry
      Citation
      Find at Harvard
      Read Now
      Purchase
      Related
      Alley, Michael, Max Biggs, Rim Hariss, Charles Herrmann, Michael Lingzhi Li, and Georgia Perakis. "Pricing for Heterogeneous Products: Analytics for Ticket Reselling." Manufacturing & Service Operations Management 25, no. 2 (March–April 2023): 409–426.
      • 2023
      • Working Paper

      Distributionally Robust Causal Inference with Observational Data

      By: Dimitris Bertsimas, Kosuke Imai and Michael Lingzhi Li
      We consider the estimation of average treatment effects in observational studies and propose a new framework of robust causal inference with unobserved confounders. Our approach is based on distributionally robust optimization and proceeds in two steps. We first... View Details
      Keywords: AI and Machine Learning; Mathematical Methods
      Citation
      Read Now
      Related
      Bertsimas, Dimitris, Kosuke Imai, and Michael Lingzhi Li. "Distributionally Robust Causal Inference with Observational Data." Working Paper, February 2023.
      • January–February 2023
      • Article

      Forecasting COVID-19 and Analyzing the Effect of Government Interventions

      By: Michael Lingzhi Li, Hamza Tazi Bouardi, Omar Skali Lami, Thomas Trikalinos, Nikolaos Trichakis and Dimitris Bertsimas
      We developed DELPHI, a novel epidemiological model for predicting detected cases and deaths in the prevaccination era of the COVID-19 pandemic. The model allows for underdetection of infections and effects of government interventions. We have applied DELPHI across more... View Details
      Keywords: COVID-19 Pandemic; Epidemics; Analytics and Data Science; Health Pandemics; AI and Machine Learning; Forecasting and Prediction
      Citation
      Read Now
      Related
      Li, Michael Lingzhi, Hamza Tazi Bouardi, Omar Skali Lami, Thomas Trikalinos, Nikolaos Trichakis, and Dimitris Bertsimas. "Forecasting COVID-19 and Analyzing the Effect of Government Interventions." Operations Research 71, no. 1 (January–February 2023): 184–201.
      • January–February 2023
      • Article

      Data-Driven COVID-19 Vaccine Development for Janssen

      By: Dimitris Bertsimas, Michael Lingzhi Li, Xinggang Liu, Jennings Xu and Najat Khan
      The COVID-19 pandemic has spurred extensive vaccine research worldwide. One crucial part of vaccine development is the phase III clinical trial that assesses the vaccine for safety and efficacy in the prevention of COVID-19. In this work, we enumerate the first... View Details
      Keywords: COVID-19; Health Testing and Trials; Forecasting and Prediction; AI and Machine Learning; Research; Pharmaceutical Industry
      Citation
      Find at Harvard
      Purchase
      Related
      Bertsimas, Dimitris, Michael Lingzhi Li, Xinggang Liu, Jennings Xu, and Najat Khan. "Data-Driven COVID-19 Vaccine Development for Janssen." INFORMS Journal on Applied Analytics 53, no. 1 (January–February 2023): 70–84.
      • 2023
      • Article

      Experimental Evaluation of Individualized Treatment Rules

      By: Kosuke Imai and Michael Lingzhi Li
      The increasing availability of individual-level data has led to numerous applications of individualized (or personalized) treatment rules (ITRs). Policy makers often wish to empirically evaluate ITRs and compare their relative performance before implementing them in a... View Details
      Keywords: Causal Inference; Heterogeneous Treatment Effects; Precision Medicine; Uplift Modeling; Analytics and Data Science; AI and Machine Learning
      Citation
      Find at Harvard
      Read Now
      Related
      Imai, Kosuke, and Michael Lingzhi Li. "Experimental Evaluation of Individualized Treatment Rules." Journal of the American Statistical Association 118, no. 541 (2023): 242–256.
      • April 12, 2022
      • Article

      Evaluation of Individual and Ensemble Probabilistic Forecasts of COVID-19 Mortality in the United States

      By: Estee Y. Cramer, Evan L. Ray, Velma K. Lopez, Johannes Bracher, Andrea Brennen, Alvaro J. Castro Rivadeneira, Michael Lingzhi Li and et al.
      Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models... View Details
      Keywords: COVID-19; Forecasting and Prediction; Health Pandemics; Mathematical Methods; Partners and Partnerships
      Citation
      Register to Read
      Related
      Cramer, Estee Y., Evan L. Ray, Velma K. Lopez, Johannes Bracher, Andrea Brennen, Alvaro J. Castro Rivadeneira, Michael Lingzhi Li, and et al. "Evaluation of Individual and Ensemble Probabilistic Forecasts of COVID-19 Mortality in the United States." e2113561119. Proceedings of the National Academy of Sciences 119, no. 15 (April 12, 2022). (See full author list here.)
      • 2022
      • Working Paper

      THEMIS: A Framework for Cost-Benefit Analysis of COVID-19 Non-Pharmaceutical Interventions

      By: Dimitris Bertsimas, Michael Lingzhi Li and Saksham Soni
      Since December 2019, the world has been ravaged by the COVID-19 pandemic, with over 150 million confirmed cases and 3 million confirmed deaths worldwide. To combat the spread of COVID-19, governments have issued unprecedented non-pharmaceutical interventions (NPIs),... View Details
      Keywords: COVID-19; Health Pandemics; Policy; Framework; Cost vs Benefits; Outcome or Result; United States; Germany; Brazil; Singapore; Spain
      Citation
      Read Now
      Related
      Bertsimas, Dimitris, Michael Lingzhi Li, and Saksham Soni. "THEMIS: A Framework for Cost-Benefit Analysis of COVID-19 Non-Pharmaceutical Interventions." Working Paper, April 2022.
      • March 2022
      • Article

      Where to Locate COVID-19 Mass Vaccination Facilities?

      By: Dimitris Bertsimas, Vassilis Digalakis Jr, Alexander Jacquillat, Michael Lingzhi Li and Alessandro Previero
      The outbreak of COVID-19 led to a record-breaking race to develop a vaccine. However, the limited vaccine capacity creates another massive challenge: how to distribute vaccines to mitigate the near-end impact of the pandemic? In the United States in particular, the new... View Details
      Keywords: Vaccines; COVID-19; Health Care and Treatment; Health Pandemics; Performance Effectiveness; Analytics and Data Science; Mathematical Methods
      Citation
      Read Now
      Related
      Bertsimas, Dimitris, Vassilis Digalakis Jr, Alexander Jacquillat, Michael Lingzhi Li, and Alessandro Previero. "Where to Locate COVID-19 Mass Vaccination Facilities?" Naval Research Logistics Quarterly 69, no. 2 (March 2022): 179–200.
      • June 2021
      • Article

      From Predictions to Prescriptions: A Data-driven Response to COVID-19

      By: Dimitris Bertsimas, Léonard Boussioux, Ryan Cory-Wright, Arthur Delarue, Vassilis Digalakis Jr, Alexander Jacquillat, Driss Lahlou Kitane, Galit Lukin, Michael Lingzhi Li, Luca Mingardi, Omid Nohadani, Agni Orfanoudaki, Theodore Papalexopoulos, Ivan Paskov, Jean Pauphilet, Omar Skali Lami, Bartolomeo Stellato, Hamza Tazi Bouardi, Kimberly Villalobos Carballo, Holly Wiberg and Cynthia Zeng
      The COVID-19 pandemic has created unprecedented challenges worldwide. Strained healthcare providers make difficult decisions on patient triage, treatment and care management on a daily basis. Policy makers have imposed social distancing measures to slow the disease, at... View Details
      Keywords: COVID-19; Health Pandemics; AI and Machine Learning; Forecasting and Prediction; Analytics and Data Science
      Citation
      Read Now
      Related
      Bertsimas, Dimitris, Léonard Boussioux, Ryan Cory-Wright, Arthur Delarue, Vassilis Digalakis Jr, Alexander Jacquillat, Driss Lahlou Kitane, Galit Lukin, Michael Lingzhi Li, Luca Mingardi, Omid Nohadani, Agni Orfanoudaki, Theodore Papalexopoulos, Ivan Paskov, Jean Pauphilet, Omar Skali Lami, Bartolomeo Stellato, Hamza Tazi Bouardi, Kimberly Villalobos Carballo, Holly Wiberg, and Cynthia Zeng. "From Predictions to Prescriptions: A Data-driven Response to COVID-19." Health Care Management Science 24, no. 2 (June 2021): 253–272.
      • 2020
      • Article

      Fast Exact Matrix Completion: A Unified Optimization Framework for Matrix Completion

      By: Dimitris Bertsimas and Michael Lingzhi Li
      We formulate the problem of matrix completion with and without side information as a non-convex optimization problem. We design fastImpute based on non-convex gradient descent and show it converges to a global minimum that is guaranteed to recover closely the... View Details
      Keywords: Mathematical Methods
      Citation
      Read Now
      Related
      Bertsimas, Dimitris, and Michael Lingzhi Li. "Fast Exact Matrix Completion: A Unified Optimization Framework for Matrix Completion." Journal of Machine Learning Research 21, no. 1 (2020).
      • May 2020
      • Article

      Scalable Holistic Linear Regression

      By: Dimitris Bertsimas and Michael Lingzhi Li
      We propose a new scalable algorithm for holistic linear regression building on Bertsimas & King (2016). Specifically, we develop new theory to model significance and multicollinearity as lazy constraints rather than checking the conditions iteratively. The resulting... View Details
      Keywords: Mathematical Methods; Analytics and Data Science
      Citation
      Read Now
      Related
      Bertsimas, Dimitris, and Michael Lingzhi Li. "Scalable Holistic Linear Regression." Operations Research Letters 48, no. 3 (May 2020): 203–208.
      • 1
      • 2
      • →
      ǁ
      Campus Map
      Harvard Business School
      Soldiers Field
      Boston, MA 02163
      →Map & Directions
      →More Contact Information
      • Make a Gift
      • Site Map
      • Jobs
      • Harvard University
      • Trademarks
      • Policies
      • Accessibility
      • Digital Accessibility
      Copyright © President & Fellows of Harvard College.