Data Sciences Speaker Series: Timothy Christensen

This event is co-organized by the U of T Data Sciences Institute and CANSSI Ontario.

Join us for the next installment of the Data Science Speaker Series with:

Timothy Christensen
Professor
Department of Economics
Yale University

Free Event | Registration Required

Talk Title:
Unstructured data in economics: Opportunities and challenges

Abstract:
Researchers across economics and related social sciences increasingly use machine learning and AI to generate new variables from unstructured data. These generated variables are typically used as inputs in downstream models. However, naively treating the generated variables as regular numerical data can lead to biased estimates and invalid inference. This talk discusses methods to debias estimates and restore valid inference when validation data are not available. We focus on two key economic applications: measuring “soft” variables for macroeconomic forecasting, and demand estimation for online platforms. We will also relate the approach prediction-powered inference, highlighting challenges that arise when, as is often the case in economics, complete validation datasets are unavailable.

Speaker Profile:
Prof. Christensen’s research interests lie broadly across theoretical and applied econometrics, financial econometrics, and statistics/data science. His most recent research is at the intersection of econometrics and machine learning, where he works on the integration of unstructured data into quantitative economic modelling. Before joining Yale, he was a Professor of Economics at University College London.