Paper     * denotes equal contributions

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. 

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]