Event

Join us for a talk plus Q&A with Dr. Julian Hsu, Staff Data Scientist at Airbnb.

Speaker: Dr. Hsu is an economist with 14+ years of experience in machine learning (ML), experimentation, and causal ML models. He is a former Sr. Economist at Amazon and holds a PhD in economics from the University of Michigan.

Talk: Dr Hsu will share his experience working as an economist/data scientist at tech companies and present a case study on which he worked and for which he and his team applied state of the art causal inference methodologies.

Audience: Students trained in fields such as economics, data science, statistics, and computer science who have an interest in causal inference and are considering a career in the tech industry.

Goal: When students transition from academia to working, they often experience a big gap between how causal inference is taught in the classroom and how it’s applied in the workplace. In the tech industry: (1) The causal question rarely comes fully formed—researchers must disambiguate the business problems. (2) Company data is messier and larger than what’s used in coursework. (3) Implementation choices depend on stakeholders, scale, and costs, not just technical correctness. (4) Communicating results effectively is as important as getting them right. This talk’s goal is to bridge the gap between theory and practice through real-world case studies.

Want more of this? This talk is one of seven talks with tech industry experts who have wide-ranging experience applying causal analysis at tech companies such as Airbnb, Amazon, Google, Meta, Twitter, Zillow, and Wayfair. This series of talks  is offered in conjunction with ECMA 31370 “Causal Analysis for Industry” taught by Dr. Melissa Tartari at the Kenneth C. Griffin Department of Economics.