Rate My Professor Hyunseung Kang

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.