Background

I am an assistant professor in the Department of Statistics at the University of Illinois Urbana-Champaign.

I was a postdoctoral researcher in the Department of Statistics and Data Science at the Wharton School, University of Pennsylvaina, mentored by Prof. Eric J. Tchetgen Tchetgen. In May 2022, I received my Ph.D. in Statistics from the University of Wisconsin-Madison where I was advised by Prof. Hyunseung Kang. Prior to joining the Ph.D. program in July 2017, I worked as a statistician for two and a half years at the Central Bank of Korea. I received a B.S. in Statistics from Seoul National University.

My research broadly focuses on (a) causal inference under interference and non-i.i.d. settings, (b) causal inference under unmeasured confounding, and (c) optimal treatment regimes and policy learning. A common theme in my research is to use non/semiparametric theory and optimization methods to develop efficient and robust estimators of causal quantities in (a)-(c).

If you are a PhD student at UIUC interested in working with me, please contact me via email.

CV

Here is the most recent version of my CV 

Employment 

• Assistant Professor, Department of Statistics, University of Illinois Urbana-Champaign (2024-)

• Postdoctoral Researcher, Department of Statistics and Data Science, University of Pennsylvania (2022-2024)

• Statistician, the Central Bank of Korea (2015-2017)

Education

• Ph.D. in Statistics, University of Wisconsin-Madison (2017-2022)

• B.S. in Statistics, minored in Economics, Seoul National University (2009-2015)

Awards and Honors

• IMS Hannan Graduate Student Travel Award (2022)

Awarded by the Institute of Mathematical Statistics for excellence in research

• ASA Epidemiology Section Student Paper Competition Winner (2022; Norman Breslow Young Investigator Award, Awarded for the Top Paper)

Awarded by the ASA Epidemiology Section for the top paper to attend JSM 2021

• IMS Lawrence D. Brown Ph.D. Student Award (2021)

Awarded by the Institute of Mathematical Statistics for excellence in research

• ASA Biometrics Section Student Paper Competition Winner (2021, Awarded for the Best Five Papers)

Awarded by the ASA Biometrics Section for the best five student papers to attend JSM 2021

• ENAR Distinguished Student Paper Award (2020)

Awarded by the Eastern North American Region (ENAR) International Biometric Society to attend ENAR 2020 Spring Meeting

• University of Wisconsin-Madison Statistics Department Teaching Award (2019)

Awarded by the Statistics Department of the University of Wisconsin-Madison for excellence in TAing a graduate-level course

• Graduated 2nd class honor (2015)

Department of Statistics, Seoul National University

• National Scholarship For Science & Engineering (2009-2015)

Awarded by the Korea Student Aid Foundation for excellence in science and engineering during undergraduate career

Paper

1. Liu, J., Park, C., Li, K., Tchetgen Tchetgen, E. (2024) Regression-Based Proximal Causal Inference. American Journal of Epidemiology. [Github]

2. Park, C., Richardson, D., Tchetgen Tchetgen, E. (2024) Single Proxy Control. Biometrics. [Github] [30-min Presentation at OCIS]

3. Gross, M., Sobecki, J., Park, C., Yu, M., Wallace, S. (2024) Risk Factors Associated with Distress Among Postoperative Patients in an Academic Gynecologic Oncology Practice. Journal of the National Comprehensive Cancer Network.

4. Tchetgen Tchetgen, E., Park, C., Richardson, D. (2024) Universal Difference-in-Differences for Causal Inference in Epidemiology. Epidemiology. [Github]

5. Wang, B., Park, C., Small, D., Li, F. (2024) Model-robust and Efficient Covariate Adjustment for Cluster-randomized Experiments. Journal of the American Statistical Association.

6. Park, C., Chen, G., Yu, M., Kang, H. (2024) Minimum Resource Threshold Policy Under Partial Interference. Journal of the American Statistical Association. [Github]

7. Park, C., Kang, H. (2023) A Groupwise Approach for Inferring Heterogeneous Treatment Effects in Causal Inference. Journal of the Royal Statistical Society, Series A (Statistics in Society). [Github]

8. Suk, Y., Park, C. (2023) Designing Optimal, Data-Driven Policies from Multisite Randomized Trials. Psychometrika. [Github]

9. Kang, H.*, Park, C.*, Trane, R.* (2023) Propensity Score Modeling: Key Challenges When Moving Beyond the No-Interference Assumption. Observational Studies. (*: equal contribution)

10. Park, C., Kang, H. (2023) Assumption-Lean Analysis of Cluster Randomized Trials in Infectious Diseases for Intent-to-Treat Effects and Network Effects. Journal of the American Statistical Association. [R package] [30-min Presentation at 2022 IMS Annual Meeting]

11. Park, C., Kang, H. (2022) Efficient Semiparametric Estimation of Network Treatment Effects Under Partial Interference. Biometrika. [Github]

Preprint

• Suk, Y., Park, C. (2025) Causal Mediation and Functional Outcome Analysis with Process Data. PsyArXiv.

• Suk, Y., Park, C., Pan, C., Kim, K. (2024) Fair and Robust Estimation of Heterogeneous Treatment Effects for Optimal Policies in Multilevel Studies. PsyArXiv. 

• Shpitser, I., Park, C., Andrews, R., Tchetgen Tchetgen, E. (2024) Modeling Interference Via Symmetric Treatment Decomposition. arXiv. 

• Park, C., Stensrud, M., Tchetgen Tchetgen, E. (2024) Proximal Causal Inference for Conditional Separable Effects. arXiv. [Github]

• Park, C., Tchetgen Tchetgen, E., (2023) Single Proxy Synthetic Control. arXiv. [Github]

• Park, C., Chen, G., Yu, M. (2023) Personalized Two-sided Dose Interval. arXiv.

• Park, C., Tchetgen Tchetgen, E. (2022) A Universal Difference-in-Differences Approach for Causal Inference. arXiv.

• Park, C., Kang, H. (2021) A More Efficient, Doubly Robust, Nonparametric Estimator of Treatment Effects in Multilevel Studies. arXiv. [Github]

• Yu, A., Park, C., Kang, H., Fletcher, J. (2021) Leveraging Machine Learning to Estimate Effect Modification. SocArXiv.