Department of Statistics
1300 University Avenue
Madison, WI 53706
(remove all marine mammals from the e-mail address)
I am currently an assistant professor in the Department of Statistics at the University of Wisconsin-Madison. 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)
Guo, Z., Kang, H., Cai, T. T., Small, D. S. (2016) Testing Endogeneity with Possibly Invalid Instruments and High Dimensional Covariates. 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.
Kang, H., Peck, L., Keele, L. (2018+). Inference for Instrumental Varaibles: A Randomization Inference Approach. Journal of the Royal Statistical Society: Series A.
Guo, Z., Kang, H., Cai, T. T., Small, D. S. (2016+). Confidence Interval for Causal Effects with Invalid Instruments using Two-Stage Hard Thresholding with Voting. Journal of the Royal Statistical Society: Series B.
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]
Honors and Awards
J. Parker Memorial Bursk Award (2014)
Awarded by the Statistics Department at the Wharton School for excellence in research
Deming Student Scholar Award (2014)
Awarded by the American Statistical Association and the American Society for Quality to recognize outstanding graduate students in applied statistics.
Health Policy Statistics Section (HPSS) Student Paper Competition Winner (2014)
Awarded by the American Statistical Association, HPSS Section to attend Joint Statistical Meeting 2014.
Thomas R. Ten Have Award (2013)
Awarded at the 2013 Atlantic Causal Inference Conference at Harvard University for
exceptionally creative or skillful research
on causal inference from a newcomer to the study of causality and statistics.
Art of Graduate Research Symposium, 2nd Place (2012)
Awarded by GAPSA for excellence in research by graduate students.
Donald S. Murray Award for Excellence in Teaching (2011)
Awarded by the Statistics Department at Wharton for excellence in teaching.
Penn Prize for Excellence in Teaching by Graduate Students (2011)
Awarded by the University of Pennsylvania for excellence in teaching.
Teaching Evaluations (score: 0-4, 4 = highest)
Spring 2013: Stat 111, Introduction to Statistics
Final Exam Review Guide
Fall 2012: Stat 550, Mathematical Statistics
Fall 2011: Stat 430, Probability
Final Exam Review Guide
Summer 2011: Stat 611, Mathematics for Business Analysis: Math Camp for MBAs
Linear Optimization Review
Fall 2010: Stat 101, Introduction to Business Statistics: Part 1
Summer 2012: Stat 431, Statistical Inference (Course Website)