This talk explores the growing intersection of Machine Learning (ML), Artificial Intelligence (AI), and modern econometric methods, with a particular focus on causal inference in empirical economic research. We will provide a practical introduction to high-dimensional function fitting techniques—commonly known as machine learning methods—that enable efficient estimation and inference of treatment effects and structural parameters. The talk will also highlight recent advances in causal machine learning, especially in the context of panel data, and examine how unsupervised learning techniques such as factor models and clustering can uncover latent structures in economic datasets. If time permits, we will touch on the emerging use of AI in economic applications.
Mingli Chen is an Associate Professor of Economics in the Department of Economics at the University of Warwick, a Research Associate in CeMMAP, and a Turing Fellow at the Alan Turing Institute (the UK's National Institute for Data Science and Artificial Intelligence). She received her PhD from Boston University and has held visiting positions at UC Berkeley, Stanford University, and the Federal Reserve Bank of Boston. Her research interests include Econometrics, Machine Learning, and AI in Economics. Her papers have been published in leading economics and statistics journals such as the Journal of Econometrics, the Journal of the Royal Statistical Society: Series B, and the Annals of Statistics. She won the LABOUR Prize at the Seventh Italian Congress of Econometrics and Empirical Economics, and received an Honorable Mention in the Arnold Zellner Thesis Award Competition by the Journal of Business and Economic Statistics in 2017. Starting in January 2024, she also serves as an Associate Editor of the Journal of Econometrics.
Host: Prof Jinlin WEI, Assistant Professor, Division of Social Science, HKUST