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.

• Observational vs Experimental Data When Making Automated Decisions Using Machine Learning (with Foster Provost). INFORMS Journal on Data Science (2025).
• 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 on Data Science (2022).
• Explaining Data-Driven Decisions made by AI Systems: The Counterfactual Approach (with Foster Provost and Xintian Han). MIS Quarterly (2022).

• Honesty in Causal Forests: When It Helps and When It Hurts (with Yanfang Hou). Major revision at Information Systems Research.
• Causal Post-Processing of Predictive Models (with Yanfang Hou, Foster Provost, and Jennifer Hill). Under review.
• Policy Learning for Payment Compliance: Out-of Time Evidence on the Value-and Limits-of ML Targeting at a Brazilian Water Utility (with Felipe A. Araujo, Juliana Dutra, David Hagmann, and Nina Mazar). Under review.
• Causal Ordering Without Effect Estimation: A Framework for Using Proxies in Treatment Prioritization (with Jorge Loría). In preparation for submission.
• Causal Inference Isn't Special: Why It's Just Another Prediction Problem. Opinion piece.

I teach courses that help business students learn to reason with data and use predictive modeling to improve decision making.

I have been the primary instructor for the following courses:

• 2023–2024, 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, Undergraduate
• 2020, Spring (NYU Stern) — Data Mining for Business Analytics, Undergraduate
• 2019, Summer (INCAE Business School)** — Predictive Analytics, MSBA

*Despite the name, “Big Data Analytics” focuses on predictive analytics.
**Selected by students as the best instructor in the program.

I have also served as a secondary instructor for:

• 2019, Spring (NYU Stern) — Data Mining for Business Analytics, MBA

And as a teaching assistant for the following courses (all at NYU Stern):

• Data Mining for Business Analytics, MBA (×2)
• Social Media and Digital Marketing, MBA (×3)
• Introduction to Business Analytics, MSBA (×3)
• Social Media and Digital Marketing, MSBA (×2)