Assistant Professor of Economics
Jianfei Cao studies applied and theoretical econometrics. His research has chiefly been in the areas of machine learning methods in economic applications, causal inference in comparative case studies, and weak identification. His most recent research has studied the use of unsupervised learning methods in forming clustering structures used in problems involving estimation and inference of causal effects.
Inference for Dependent Data with Learned Clusters
Jianfei Cao, Christian Hansen, Damian Kozbur, and Lucciano Villacorta, Jul 2021. Revisions requested at Review of Economics and Statistics.
Synthetic Control Inference for Staggered Adoption: Estimating the Dynamic Effects of Board Gender Diversity Policies
Jianfei Cao, Shirley Lu, Dec 2019. [Matlab]
Estimation and Inference for Synthetic Control Methods with Spillover Effects
Jianfei Cao, Connor Dowd, Feb 2019. [Matlab]
On the Empirical Content of the Beckerian Marriage Model
Principal Component and Static Factor Analysis
Jianfei Cao, Chris Gu and Yike Wang, in Macroeconomic Forecasting in the Era of Big Data ed. by Peter Fuleky, 2020, 229-266. Advanced Studies in Theoretical and Applied Econometrics, vol 52, Springer, Cham.
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PhD 2021, University of Chicago Booth School of Business
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