Hello! I am a data scientist working on the use of machine learning for large-scale intervention decisions and an assistant professor of Information Systems at the HKUST Business School.

Most of my work reveals, analyzes, and illustrates the circumstances in which good intervention decisions can be made using machine learning models, even if the models are problematic for causal-effect estimation. This has important implications because acquiring data to estimate causal effects accurately is often complicated and expensive, and results are often better when modeling intervention decisions rather than causal effects.

I earned my PhD degree in Information Systems from the NYU Stern School of Business, my MBA degree from the INCAE Business School, and my undergraduate degree in Computer Science from the Tecnológico de Costa Rica.

• A Comparison of Methods for Treatment Assignment with an Application to Playlist Generation (with Foster Provost, Jesse Anderton, Benjamin Carterette, and Praveen Chandar). Information Systems Research (2023).
• Evolution of Referrals Over Customers' Life Cycle: Evidence from a Ride-Sharing Platform (with Maxime Cohen and Anindya Ghose). Information Systems Research (2023) .
• Causal Classification: Treatment Effect Estimation vs. Outcome Prediction (with Foster Provost). Journal of Machine Learning Research (2022).
• Causal Decision Making and Causal Effect Estimation Are Not the Same... and Why It Matters (with Foster Provost). INFORMS Journal of Data Science (2022).
• Explaining Data-Driven Decisions made by AI Systems: The Counterfactual Approach (with Foster Provost and Xintian Han). MIS Quarterly (2022).

• Observational vs Experimental Data When Making Automated Decisions Using Machine Learning (with Foster Provost). Under review, after major revision.
• Inferring Effect Ordering Without Causal Effect Estimation (with Jorge Loría). Under review.
• Causal Fine-Tuning and Effect Calibration of Non-Causal Predictive Models. (with Yanfang Hou, Foster Provost, and Jennifer Hill). In preparation for journal submission.

I created several innovative case studies for teaching data science to business students in practical settings. They are available here. Please feel free to use them in your courses too! And if you like the material or have any suggestions, please make sure to reach out. I am always happy to learn that my work was useful to others.

I have been the primary instructor for the following courses:

• 2023, Fall (HKUST Business School) --- Big Data Analytics*, MSc. in Business Analytics, MSc. in Information Systems, MSc. in Marketing
• 2022, Fall (HKUST Business School) --- Big Data Analytics*, MSc. in Business Analytics, MSc. in Information Systems
• 2021, Fall (HKUST Business School) --- Big Data Analytics*, MBA
• 2021, Fall (HKUST Business School) --- Data Mining for Business Analytics, UG
• 2020, Spring (NYU Stern) --- Data Mining for Business Analytics, UG
• 2019, Summer (INCAE Business School)** --- Predictive Analytics, MSBA

*Despite the name, the course is about predictive analytics (not big data).
**Selected by students as the best instructor in the program.

And the secondary instructor for the following course:

• 2019, Spring (NYU Stern) --- Data mining for Business Analytics, MBA

I have also been a teaching assistant for the following courses (all at NYU Stern):

• Data mining for Business Analytics, MBA (x2)
• Social Media and Digital Marketing, MBA (x3)
• Introduction to Business Analytics, MSBA (x3)
• Social Media and Digital Marketing, MSBA (x2)