Yong Cai

Yong Cai is an Assistant Professor at the University of Wisconsin-Madison, specializing in theoretical and applied econometrics. His research bridges empirical methods, social networks, and policy evaluation.

Key research areas include:

• Statistical inference with clustered and sparse network data
• Regression discontinuity designs accounting for spillovers
• Social disruption in policy interventions
• Machine learning applications in econometric modeling

Recent work focuses on:

• Developing novel randomization tests for small-sample clustering
• Quantifying social disruption through network adjacency eigenvalues
• Improving finite-sample properties of treatment effect estimators
• Exploring information content in monetary policy announcements

He has contributed to computational tools, including the MatrixHTE R package for heterogeneous treatment effect analysis, and actively collaborates on methodological extensions with researchers like Eric Auerbach and Santiago Camara.