Hyunseung Kang

Assistant Professor
Department of Statistics
University of Wisconsin-Madison

Department of Statistics
1220 Medical Sciences Center
1300 University Avenue
Madison, WI 53706

Github: hyunseungkang
E-mail: hyunseungWHALE@statWHALE.wisc.edu
(remove all marine mammals from the e-mail address)


I am an assistant professor in the Department of Statistics at the University of Wisconsin-Madison. I am also an affiliate of the Department of Biostatics and Medical Informatics (BMI), the Center for Demography and Ecology (CDE) and the Center for Demogrophy of Health and Aging (CDHA). From 2015 to 2016, I did my postdoc in Economics at the Stanford Graduate School of Business, advised by Professor Guido Imbens. In 2015, I received my Ph.D. in Statistics at the Wharton School of Business of the University of Pennsylvania and I was co-advised by Professors T. Tony Cai and Dylan S. Small.

Broadly speaking, my research is focused on developing theory and methods to analyze causal relationships by using instrumental variables, econometrics, semi/nonparametric methods, network analysis, and machine learning. I am interested in applications to genetics, epidemiology, infectious diseases, health policy, education, and applied microeconomics.

I am currently an associate editor of Biometrics.


NSF Postdoc, Stanford Graduate School of Business, Stanford University (2015-2016)

Ph.D. Statistics, The Wharton School of Business, University of Pennsylvania (2010-2015)

M.S. Statistics, Stanford University (2009-2010)

B.S. Mathematical and Computational Science, Stanford University (2006-2010)


Park, C., Chen, G., Yu, M., Kang, H. (2021) Optimal Allocation of Water and Sanitation Facilities to Prevent Communicable Diarrheal Diseases in Senegal Under Partial Interfernce. arXiv.

Park, C. and Kang, H. (2021) More Efficient, Doubly Robust, Nonparametric Estimators of Treatment Effects in Multilevel Studies. arXiv.

Suk, Y., Steiner, P., Kim, J.S., Kang, H. (2021) Regression Discontinuity Designs with an Ordinal Running Variable: Evaluating the Effects of Extended Time Accommodations for English Language Learners. PsyArXiv.

Suk, Y. and Kang, H. (2021+) Tuning Random Forests for Causal Inference Under Cluster-Level Unmeasured Confounding. Multivariate Behavioral Research.

Park, C. and Kang, H. (2021+) Assumption-Lean Analysis of Cluster Randomized Trials in Infectious Diseases for Intent-to-Treat Effects and Network Effects. Journal of the American Statistical Association.

Johnson, M., Cao, J., Kang, H. (2021+) Detecting Heterogeneous Treatment Effects with Instrumental Variables and Application to the Oregon Health Insurance Experiment. Annals of Applied Statistics.

Suk, Y. and Kang, H. (2021+) Robust Machine Learning for Treatment Effects in Multilevel Observational Studies Under Cluster-level Unmeasured Confounding. Psychometrika.

Wang, S., Kang, H. (2021+) Weak-Instrument Robust Tests in Two-Sample Summary-Data Mendelian Randomization. Biometrics.

Heng, S.*, Kang, H.*, Small, D. S., Fogarty, C. B. (2021) Increasing Power for Observational Studies of Aberrant Response: An Adaptive Approach. Journal of the Royal Statistical Society: Series B, 83(3):482-504. (*: equal contribution).

Ye, T., Shao, J., Kang, H. (2021) Debiased Inverse-Variance Weighted Estimator in Two-Sample Summary-Data Mendelian Randomization. Annals of Statistics, 49(4):2079-2100.

Suk, Y., Kim, J-S, Kang, H. (2021) Hybridizing Machine Learning Methods and Finite Mixture Models for Estiamting Heterogeneous Treatment Effects in Latent Classes. Journal of Educational and Behavioral Statistics, 46(3):323-347.

Kang, H., Jiang, Y., Zhao, Q., Small, D. S. (2021) ivmodel: An R Package for Inference and Sensitivity Analysis of Instrumental Variables Models with One Endogeneous Variable. Observational Studies, 7:1-24.

Panyard, D., Kim, K. M., Darst, B. F., Deming, Y. K., Zhong, X., Wu, Y., Kang, H., Carlsson, C. M., Johnson, S.C., Asthana, S.A., Engleman, C.D., Lu, Q. (2021) Cerebrospinal fluid metabolomics identifies 19 brain-related phenotype associations. Communications Biology, 4:63.1-11.

Park, C., Kang, H. (2020) Efficient Semiparametric Estimation of Network Treatment Effects Under Partial Interfernce. arXiv.

Bi, N., Kang, H., Taylor, J. (2020) Inferring Treatment Effects After Testing Instrument Strength in Linear Models. arXiv.

Kang, H.*, Lee, Y.*, Cai, T.T., Small, D.S. (2020) Two Robust Tools for Inference about Causal Effects with Invalid Instruments. Biometrics. (*: equal contribution).

Suk, Y., Kang, H., Kim, J-S. (2020) Random Forests Approach for Causal Inference with Clustered Observational Data. Multivariate Behavioral Research. 1-24.

Athey, S., Chetty, R., Imbens, G., Kang, H. (2019) The Surrogate Index: Combining Short-Term Proxies to Estimate Long-Term Treatment Effects More Rapidly and Precisely. NBER Working Paper. No. 26463

Bi, N., Kang, H., Taylor, J. (2019) Inference After Selecting Plausibly Valid Instruments with Application to Mendelian Randomization. arXiv.

Park, C., Kang, H. (2019) A Groupwise Approach for Inferring Heterogeneous Treatment Effects in Causal Inference. arXiv.

You, J. C., Jones, E., Cross, D. E., Lyon, A. C., Kang, H., Newberg, A. B., Lippa, C. F. (2019) Association of beta-Amyloid Burden With Sleep Dysfunction and Cognitive Impairment in Elderly Individuals With Cognitive Disorders. Journal of the American Medical Association: Network Open, 2(10):e1913383. 1-12.

Hu, B., Shen, N., Li, J., Kang, H., Hong, J., Fletcher, J., Greenberg, J., Mailick, M., and Lu, Q. (2019) Genome-wide association study reveals sex-specific genetic architecture of facial attractiveness. PLoS Genetics, 15.4:1-18.

Yan, D., Hu, B., Darst, B., Mukherjee, S., Kunkle, B., Deming, Y., Dumitrescu, L., Wang, Y., Naj, A., Kuzma, A., Zhao, Y., Kang, H., Johnson, S., Cruchaga, C., Hohman, T., Crane, P., Engelman, C., Alzheimer's Disease Genetics Consortium (ADGC), Lu, Q. (2018). Biobank-wide association scan identifies risk factors for late-onset Alzheimer's disease and endophenotypes. bioRxiv.

Kang, H., Keele, L. (2018) Spillover Effects in Cluster Randomized Trials with Noncompliance. arXiv.

Kang, H., Keele, L. (2018) Estimation Methods for Cluster Randomized Trials with Noncompliance: A Study of A Biometric Smartcard Payment System in India. arXiv.

Guo, Z., Kang, H., Cai, T. T., Small, D. S. (2018) Testing Endogeneity with High Dimensional Covariates. Journal of Econometrics, 207:175-187.

Kang, H., Peck, L., Keele, L. (2018) Inference for Instrumental Varaibles: A Randomization Inference Approach. Journal of the Royal Statistical Society: Series A, 181, 1231-1254.

Guo, Z., Kang, H., Cai, T. T., Small, D. S. (2018) Confidence Interval for Causal Effects with Invalid Instruments using Two-Stage Hard Thresholding with Voting. Journal of the Royal Statistical Society: Series B, 80:793-815.

Kang, H., Imbens, G. (2016) Peer Encouragement Designs in Causal Inference with Partial Interference and Identification of Local Average Network Effects. arXiv.

Kang, H. (2016) Commentary: Matched Instrumental Variables: A Possible Solution to Severe Confounding in Matched Observational Studies? Epidemiology,27, 624-632.

Kang, H., Kreuels, B., May, J., Small, D. S. (2016) Full Matching Approach to Instrumental Variables Estimation with Application to the Effect of Malaria on Stunting. Annals of Applied Statistics,10,335-364.

Kang, H., Zhang, A., Cai, T. T., Small, D. S. (2016) Instrumental Variables Estimation with Some Invalid Instruments and its Application to Mendelian Randomization. Journal of the American Statistical Association,111, 132-144.

Kang, H., Kreuels, B., Adjei, O., Krumkamp, R., May, J., Small, D. S. (2013) The Causal Effect of Malaria on Stunting: A Mendelian Randomization and Matching Approach. International Journal of Epidemiology,42,1390-1398.


All the software is available on GitHub: [link]