Research Interests
- Causal Inference
- Machine Learning
- Mediation Analysis
- Dimension Reduction/Variable Selection
Publications
1. Propensity Score-Based Methods
1. Propensity Score-Based Methods
- Lin, L., Zhu, Y. & Chen, L. (2018), Causal inference for multi-level treatments with machine-learned propensity scores, Health Services & Outcomes Research Methodology, accepted.
- Zhu, Y., & Lin, L. (2016), Propensity score modeling and evaluation. In H. He, P. Wu, & D.-G. Chen (Eds.), Statistical Causal Inferences and Their Applications in Public Health Research. Springer.
- Zhu, Y., Schonbach, M., Coffman, D.L., Williams J.S. (2015), Variable Selection for Propensity Score Estimation via Balancing Covariates, Epidemiology, 26(2), e14-15.
- Zhu, Y., Coffman, D.L. and Ghosh, D. (2015), A Boosting Algorithm for Estimating Generalized Propensity Scores with Continuous Treatments, Journal of Causal Inference, 3(1), 25-40.
- Zhu, Y., Ghosh, D., Mitra, N. and Mukherjee, B. (2014), A Data-Adaptive Strategy for Inverse Weighted Estimation of Causal Effects, Health Services & Outcomes Research Methodology, 14(3), 69-91.
- Luo, W. and Zhu, Y. (2019), Matching Using Sufficient Dimension Reduction for Causal Inference, Journal of Business and Economic Statistics, accepted.
- Luo, W., Wu, W. and Zhu, Y. (2019), Learning Heterogeneity in Causal Inference Using Sufficient Dimension Reduction, Journal of Causal Inference, 7(1).
- Luo, W., Zhu, Y. and Ghosh, D. (2017), On Estimating Regression-based Causal Effects Using Sufficient Dimension reduction, Biometrika, 104(1), 51-65.
- Ghosh, D, Zhu, Y., and Coffman, D.L. (2015), Penalized Regression Procedures for Variable Selection in the Potential Outcomes Framework, Statistics in Medicine, 34(10), 1645-1658.
- Xie, Y., Cotton, C. and Zhu, Y. (2020), Multiply Robust Estimation of Causal Quantile Treatment Effects, Statistics in Medicine, 39 (28), 4238--4251.
- Zhu, Y., Savage, J.S., and Ghosh, D. (2018), A Kernel-based metric for balance assessment, Journal of Causal Inference, 6(2).
- Xie, Y., Zhu, Y., Cotton, C. and Wu, P. (2017), A model averaging approach for estimating propensity scores by optimizing balance, Statistical Methods in Medical Research, doi:10.1177/0962280217715487.
- Ye, Z., Zhu, Y. and Coffman, D. (2021). Variable Selection for Causal Mediation Analysis Using LASSO-based Methods, Statistical Methods in Medical Research, 30 (6), 1413--1427.
- Coffman, D. L., MacKinnon, D. P., Zhu, Y., & Ghosh, D. (2016), A comparison of potential outcomes approaches for assessing causal mediation. In H. He, P. Wu, & D.-G. Chen (Eds.), Statistical Causal Inferences and Their Applications in Public Health Research. Springer.
- Zhu, Y., Ghosh, D., Coffman, D. L., Savage J. S. (2016), Estimating Controlled Direct Effects of Restrictive Feeding Practices in the `Early Dieting in Girls' Study, Journal of Royal Statistical Society, Series C, 65(1), 115-130.
- Costello, J., Li, Y., Zhu, Y. et al. (2021). Using Conventional and Machine learning Propensity Score Methods to Examine the Effectiveness of 12-step Group Involvement Following Inpatient Addiction Treatment, Drug and Alcohol Dependence, 227, 168943.
- Arani, A., Hu, P., Zhu, Y. (2021). Fairness-Aware Link Optimization for Space-Terrestrial Integrated Networks: A Reinforcement Learning Framework, IEEE Access, 977624--77636.
- Arani, A., Azari, M. , Hu, P., Zhu, Y. et al. (2021). Reinforcement Learning for Energy-Efficient Trajectory Design of UAVs, Internet of Things.
- Arani, A., Hu, P., Zhu, Y. (2021). Re-envisioning Space-Air-Ground Integrated Networks: Reinforcement Learning for Link Optimization, IEEE International Conference on Communications (ICC): Next-Generation Networking and Internet Symposium, 1--7.