Research Interests

• Automated decision making
• Statistical inference for treatment assignment policies
• Causal inference
• Interpretable machine learning

Academic Qualification

• PhD 2021 New York University, Information Systems
• MBA 2015 INCAE Business School
• B.Sc. 2011 Instituto Tecnológico de Costa Rica, Computer Science

Academic And Professional Experience

• Assistant Professor, The Hong Kong University of Science and Technology, 2021-present

SELECTED PUBLICATIONS

• Fernández-Loría, C., Provost, F., Anderton, J., Carterette, B., and Chandar, P., 2023. “A Comparison of Methods for Treatment Assignment with an Application to Playlist Generation,” Information Systems Research, 34(2), pp.786-803.
• Fernández-Loría, C., Cohen, M.C., and Ghose, A., 2023. “Evolution of Referrals Over Customers' Life Cycle: Evidence from a Ride-Sharing Platform,” Information Systems Research, 34(2), pp.698-720.
• Fernández-Loría, C., and Provost, F., 2022. “Causal Classification: Treatment Effect Estimation vs. Outcome Prediction,” Journal of Machine Learning Research, 23(59), pp.1-35.
• Fernández-Loría, C., and Provost, F., 2022. “Causal Decision Making and Causal Effect Estimation Are Not The Same... and Why It Matters,” INFORMS Journal on Data Science, 1(1), pp.4-16.
• Fernández-Loría, C., Provost, F., and Han, X., 2022. “Explaining Data-Driven Decisions Made by AI Systems: The Counterfactual Approach,” MIS Quarterly, 46(3), pp.1635-1660.