Rate My Professor Hyunseung Kang

Hyunseung Kang is an Associate Professor in the Department of Statistics at the University of Wisconsin-Madison, where he joined in 2017. He holds affiliate appointments in the Department of Educational Psychology (Quantitative Methods program), Department of Biostatistics and Medical Informatics, Center for Demography and Ecology, and Center for Demography of Health and Aging. Kang earned his Ph.D. in Statistics from the Wharton School of Business at the University of Pennsylvania in 2015, co-advised by Professors T. Tony Cai and Dylan S. Small. Prior to his doctoral studies, he received both an M.S. in Statistics and a B.S. in Mathematical and Computational Science from Stanford University in 2010. Following his Ph.D., Kang served as an NSF Mathematical Sciences Postdoctoral Research Fellow in Economics at the Stanford Graduate School of Business from 2015 to 2016.

Kang's academic interests center on causal inference, with a focus on developing theoretical foundations and statistical methods to uncover causal relationships from large, complex observational data. His approaches integrate instrumental variables analysis, econometric tools, matching techniques, and machine learning to tackle issues like unmeasured confounding, invalid instruments, and partial interference. These methodologies find applications across genetics, epidemiology, health policy, education, sociology, and economics. Prominent publications include 'The Surrogate Index: Combining Short-Term Proxies to Estimate Long-Term Treatment Effects More Rapidly and Precisely' (Review of Economic Studies, 2025+, with Athey, Chetty, Imbens); 'A More Credible Approach to Multivariable Mendelian Randomization' (Biometrika, 2025+, with Wu, Ye); 'Identification and Inference with Invalid Instruments' (Annual Review of Statistics and Its Applications, 2025, with Guo, Liu, Small); 'Minimum Resource Threshold Policy Under Partial Interference' (Journal of the American Statistical Association, 2024, with Park, Chen, Yu); 'Statistical Mapping of PFOA and PFOS in Groundwater throughout the Contiguous United States' (Environmental Science & Technology, 2024, with Park, Zahasky); 'Instrumental Variables Estimation with Some Invalid Instruments and its Application to Mendelian Randomization' (Journal of the American Statistical Association, 2016, with Zhang, Cai, Small); and 'Full Matching Approach to Instrumental Variables Estimation with Application to the Effect of Malaria on Stunting' (Annals of Applied Statistics, 2016, with Kreuels, May, Small). Kang's research provides robust tools for causal estimation in challenging settings, contributing significantly to advancements in statistical methodology for observational studies.