Research

Publications:

• Cui, Y., Hannig, J., Kosorok, M. R. A unified fiducial approach to interval-censored data. Journal of the American Statistical Association. In press. 

• Cui, Y., Tchetgen Tchetgen, E. J. Selective machine learning of doubly robust functionals. Biometrika. In press.

• Cui, Y., Hannig, J. Demystifying inferential models and confidence curves: A fiducial perspective. Statistical Science. In press.

• Tchetgen Tchetgen, E. J., Ying, A., Cui, Y., Shi, X., Miao, W. An introduction to proximal causal inference. Statistical Science. In press.

• Cui, Y., Pu, H., Shi, X., Miao, W., Tchetgen Tchetgen, E. J. Semiparametric proximal causal inference. Journal of the American Statistical Association. In press.

• Cui, Y., Hannig, J. A fiducial approach to nonparametric deconvolution problem: discrete case. Science China Mathematics. In press.

• Han, S., Wu, P., Wang, Z., Cui, Y., Yang, H., Zhou, Z., Han, L., Yang, L., Jia, J., Shao, R., Wang, C. Reporting Studies Involving Cluster Data: Interpretation and Extension of the TRIPOD-Cluster Checklist. (in Chinese) Digital Medicine and Health. In press.

• Michael, H., Cui, Y., Lorch, S., Tchetgen Tchetgen, E. J. Instrumental variable estimation of marginal structural mean models for time-varying treatment. Journal of the American Statistical Association. In press.

• Ogbagaber, S., Cui, Y., Li, K., Iannotti, R. J., Albert, P. S. (2024) A hidden Markov modeling approach combining objective measures of activity and self-reported sleep to estimate the sleep-wake cycle. Journal of Applied Statistics, 51(2), 370-387.

• Shen, T., Cui, Y. (2023) Optimal treatment regimes for proximal causal learning. NeurIPS.

• Yang, H., Qi, Z., Cui, Y., Chen, P. (2023) Pessimistic model selection for deep reinforcement learning. UAI.

• Sverdrup, E., Cui, Y. (2023) Proximal causal learning of conditional average treatment effects. ICML.

• Cui, Y., Gong, R., Hannig, J., Hoffman, K. (2023) Technical Comment on “Policy impacts of statistical uncertainty and privacy”. Science, 380(6648).

• Cui, Y., Kosorok, M. R., Sverdrup, E., Wager, S., Zhu, R. (2023) Estimating heterogeneous treatment effects with right-censored data via causal survival forests. Journal of the Royal Statistical Society: Series B, 85(2), 179-211.

• Cui, Y., Michael, H., Tanser, F., Tchetgen Tchetgen, E. J. (2023) Instrumental variable estimation of the marginal structural Cox model for time-varying treatments. Biometrika, 110(1), 101-118. 

• Loyal J. D., Zhu, R., Cui, Y., Zhang, X. (2022) Dimension reduction forests: local variable importance using structured random forests. Journal of Computational and Graphical Statistics, 31(4), 1104-1113.

• Sun, B., Cui, Y., Tchetgen Tchetgen, E. J. (2022) Selective machine learning of average treatment effect with an invalid instrumental variable. Journal of Machine Learning Research, 23(204), 1-40.

• Cui, Y., Zhu, R., Zhou, M., Kosorok, M. R. (2022) Consistency of survival tree and forest models: splitting bias and correction. Statistica Sinica, 32(3), 1245-1267.

• Cui, Y. (2021) Individualized decision making under partial identification: three perspectives, two optimality results, and one paradox. Harvard Data Science Review, 3(3), 1-19.

• Cui, Y., Tchetgen Tchetgen, E. J. (2021) On a necessary and sufficient identification condition of optimal treatment regimes with an instrumental variable. Statistics and Probability Letters, 178, 109180.

• Cui, Y., Tchetgen Tchetgen, E. J. (2021) Machine intelligence for individualized decision making under a counterfactual world: A rejoinder. Journal of the American Statistical Association, 116(533), 200-206.

• Cui, Y., Tchetgen Tchetgen, E. J. (2021) A semiparametric instrumental variable approach to optimal treatment regimes under endogeneity (with discussion). Journal of the American Statistical Association, 116(533), 162-173.

• Cui, Y., Hannig, J. (2019) Rejoinder to "Nonparametric generalized fiducial inference for survival functions under censoring". Biometrika, 106(3), 527-531. 

• Cui, Y., Hannig, J. (2019) Nonparametric generalized fiducial inference for survival functions under censoring (with discussion). Biometrika, 106(3), 501-518. 

• Cui, Y., Ogbagaber, S., Hung, H. M. (2019) Rejoinder to "Statistical inference problems in sequential parallel comparison design". Journal of Biopharmaceutical Statistics, 29(6), 1134-1136.

• Cui, Y., Ogbagaber, S., Hung, H. M. (2019) Statistical inference problems in sequential parallel comparison design (with discussion). Journal of Biopharmaceutical Statistics, 29(6), 1116-1129.

• Beyhaghi, H., Lich, K. H., Cui, Y., Kosorok, M. R. (2018) Recursively imputed survival trees for extrapolating survival beyond clinical trial follow up: A simulation study. Value in Health, 21, S223.

• Rieger, T. R., Allen, R. J., Bystricky, L., Chen, Y., Colopy, G., Cui, Y., Gonzalez, A., Liu, Y., White, R., Everett, R., Banks, H. T., Musante, C. J. (2018) Improving the generation and selection of virtual populations in quantitative systems pharmacology models. Progress in Biophysics and Molecular Biology, 139: 15-22.

• Lawson, M. T., Cho, H., Choudhury, A., Cui, Y., Jiang, X., Pokaprakarn, T., Kosorok, M. R. (2018) Discussion of Laber et al. “Optimal treatment allocations in space and time for on-line control of an emerging infectious disease”. Journal of the Royal Statistical Society Series C, 67: 779-780.

• Cui, Y., Zhu, R., Kosorok, M. R. (2017) Tree based weighted learning for estimating individualized treatment rules with censored data. Electronic Journal of Statistics, 11(2): 3927-3953. 

Book Chapters:

• Shen, T., Cui, Y. Statistical reinforcement learning and dynamic treatment regimes. in Statistics in Precision Health: Theory, Methods and Applications, Springer. In press.

• Cui, Y., Xie, M. (2023) Confidence distribution and distribution estimation for modern statistical inference. in Handbook of Engineering Statistics, 2nd ed, Springer.

• Zhu, R., Formentini, S., Cui, Y. (2023) Random forests for survival analysis and high-dimensional data. in Handbook of Engineering Statistics, 2nd ed, Springer.