## Background

I am currently 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), 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, and high dimensional inference. I am interested in applications to genetics, epidemiology, health policy, education, and economics.

I am currently an associate editor for Biometrics.

## Education

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)

## Papers

Suk, Y. and Kang, H. (2020) Tuning Random Forests for Causal Inference Under Cluster-Level Unmeasured Confounding. * PsyArXiv*.

Suk, Y. and Kang, H. (2020) Robust Machine Learning for Treatment Effects in Multilevel Observational Studies Under Cluster-level Unmeasured Confounding. * PsyArXiv*.

Kang, H., Lee, Y., Cai, T.T., Small, D.S. (2020) Two Robust Tools for Inference about Causal Effects with Inavlid Instruments. * arXiv *.

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*.

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. (2020) Cerebrospinal fluid metabolomics identifies 19 brain-related phenotype associations. * bioRxiv*.

Kang, H., Jiang, Y., Zhao, Q., Small, D. S. (2020) ivmodel: An R Package for Inference and Sensitivity Analysis of Instrumental Variables Models with One Endogeneous Variable. * arXiv* .

Ye, T., Shao, J., Kang, H. (2020+) Debiased Inverse-Variance Weighted Estimator in Two-Sample Summary-Data Mendelian Randomization. * Annals of Statistics*.

Suk, Y., Kim, J-S, Kang, H. (2020) Hybridizing Machine Learning Methods and Finite Mixture Models for Estiamting Heterogeneous Treatment Effects in Latent Classes. * Journal of Educational and Behavioral Statistics*.

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

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*.

Wang, S., Kang, H. (2019) Weak-Instrument Robust Tests in Two-Sample Summary-Data Mendelian Randomization. * arXiv*.

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

Johnson, M., Cao, J., Kang, H. (2019) Detecting Heterogeneous Treatment Effect with Instrumental Variables. * arXiv*.

Heng, S., Kang, H., Small, D. S., Fogarty, C. B. (2019) Increasing Power for Observational Studies of Aberrant Response: An Adaptive Approach. * 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.

## Software

All the software is available on GitHub: [link]