*: equally contributing co-first authors

Working Papers

Bong, S., and Lee, K. (2025+). Local causal effects with continuous exposures: A matching estimator for the average derivative effect. Submitted. arXiv

Lee, S., and Lee, K. (2025+). Evaluating time-specific treatment effects in matched-pairs studies. Submitted. arXiv

Jang, J., Kim, S., and Lee, K. (2025+). Improving causal estimation by mixing samples to address weak overlap in observational studies. Submitted. arXiv

Lee, K., Bargagli-Stoffi, F. J., and Dominici, F. (2021+). Causal rule ensemble: Interpretable inference of heterogeneous treatment effects in observational studies. Submitted. arXiv

Published

-2025

Lee, Z., and Lee, K. (2025). Causal interaction and effect modification: A randomization-based approach to inference. Journal of the Korean Statistical Society, to appear.

Jung, D., Yoon, Y., and Lee, K. (2024). Analyzing the causal impact of streaming service usage on IPTV viewing. The Korean Journal of Applied Statistics, 37(5), 675–690. Paper

Im, Y.*, Lee, K.*, Lee, S.*, Shin, S., Choi, Y., Lee, J., Oh, K., Kim, J., Oh, Y., Lee, H., and Park, H. (2024). A causal inference analysis of the radiologic progression in the chronic obstructive pulmonary disease. Scientific Reports, 14(1), 17838. Paper

Bong, S., Lee, K., and Dominici, F. (2024). Differential recall bias in estimating treatment effects in observational studies. Biometrics, 80(2). Paper

Kong, I., Park, Y., Jung, J., Lee, K., and Kim, Y. (2023). Covariate balancing using the integral probability metric for causal inference. International Conference on Machine Learning, 17430–17461. Paper

Lee, K., Bhattacharya, B. B., Qin, J., and Small, D. S. (2023). A nonparametric binomial likelihood approach for causal inference in instrumental variable models. Journal of the Korean Statistical Society, 52(4), 1055–1077. Paper

Fogarty, C. B., Lee, K., Kelz, R. R., and Keele, L. (2021). Biased encouragements and heterogeneous effects in an instrumental variable study of emergency general surgical outcomes. Journal of the American Statistical Association, 116(536), 1625–1636. Paper

Lee, K., Small, D. S., and Dominici, F. (2021). Discovering heterogeneous exposure effects using randomization inference in air pollution studies. Journal of the American Statistical Association, 116(534), 569–580. arXiv Paper

Heo, S., Nori-Sarma, A., Lee, K., Benmarhnia, T., Dominici, F., and Bell, M. L. (2019). The use of a quasi-experimental study on the mortality effect of a heat wave warning system in South Korea. International Journal of Environmental Research and Public Health, 16(12), 2245. Paper

Lee, K., Lorch, S. A., and Small, D. S. (2019). Sensitivity analyses for average treatment effects when outcome is censored by death in instrumental variable models. Statistics in Medicine, 38(13), 2303–2316. arXiv Paper

Lee, K., and Small, D. S. (2019). Estimating the malaria attributable fever fraction accounting for fever killing parasites and measurement error. Journal of the American Statistical Association, 114(525), 79–92. arXiv Paper

Billig, E. B., Lee, K., Roy, J. A., Small, D. S., Ross, M. E., Castillo-Neyra, R., and Levy, M. Z. (2018). Risk maps for cities: Incorporating streets into geostatistical models. Spatial and Spatio-temporal Epidemiology, 27, 47–59. Paper

Lee, K., Small, D. S., and Rosenbaum, P. R. (2018). A powerful approach to the study of moderate effect modification in observational studies. Biometrics, 74(4), 1161–1170. arXiv Paper

Lee, K., Small, D. S., Hsu, J. Y., Silber, J. H., and Rosenbaum, P. R. (2018). Discovering effect modification in an observational study of surgical mortality at hospitals with superior nursing. Journal of the Royal Statistical Society, Series A, 181(2), 535–546. arXiv Paper