I am currently an assistant professor in the Department of Statistics at the University of Wisconsin-Madison. I am also an affiliate for 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 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 T. Tony Cai and Dylan S. Small.
Broadly speaking, my research is focused on developing theory and methods to analyze causal relationships in large observational data by leveraging instrumental variables, econometrics, high dimensional inference, and causal inference. I am interested in applications to genetics, epidemiology, health policy research, and economics.
I am currently an associate editor for 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)
Kang, H., Keele, L. (2018) Spillover Effects in Cluster Randomized Trials with Noncompliance. arXiv.
Hu, B., Shen, N., Li, J., Kang, H., Hong, J., Fletcher, J., Greenberg, J., Mailick, M., and Lu, Q. (2018) Genome-wide association study reveals sex-specific genetic architecture of facial attractiveness. bioRxiv.
Kang, H., Keele, L. (2018) Estimation Methods for Cluster Randomized Trials with Noncompliance: A Study of A Biometric Smartcard Payment System in India. arXiv.
Kang, H., Imbens, G. (2016) Peer Encouragement Designs in Causal Inference with Partial Interference and Identification of Local Average Network Effects. arXiv.
Athey, S., Chetty, R., Imbens, G., Kang, H. (2016) Estimating Treatment Effects using Multiple Surrogates: The Role of the Surrogate Score and the Surrogate Index. arXiv.
Kang, H., Cai, T. T., Small, D. S. (2016) A Simple and Robust Confidence Interval for Causal Effects with Possibly Invalid Instruments. arXiv.
Kang, H., Jiang, J., and Small, D. S. (2015) ivmodel: An R Package for Inference and Sensitivity Analysis of Instrumental Variables Models with One Endogenous Variable. arXiv with R package ivmodel.
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. (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]