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Causal Inference

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Causal Inference

Professor Zhou has made outstanding achievements in the research on Identification of Causal Effect, Robustness of Estimators, Causal Inference with Truncation-by-Death, Causal Inference of Randomized Encouragement Designs, Precision Medicine. Among them, Professor Zhou proposed to use instrumental variable method to combine the intended treatment effect with the real treatment effect, using Bayesian method for inference and sensitivity analysis, put forward a series of new methods and theories of correlation causal effect estimation. In addition, in terms of precision medicine, Professor Zhou first proposed the use of Biomarker Adjusted Treatment Effect (BATE) curve and Covariate-Specific Treatment Effect (CSTE) curve to represent the conditional average treatment effect under a given biomarker level,and it provides a uniform inferential tool in making individualized treatment decisions, and strictly proved the mathematical properties of the proposed new statistical method. Then, Professor Zhou further proposed the method of CSTE curve with binary outcome variable and Confidence Band, and extended the CSTE curve to high-dimensional covariable scenarios, and optimaized its mathematical theory.

Selected Publications:
  1. Miao W., Hu W., Ogburn E.L., Zhou, XH. Identifying Effects of Multiple Treatments in the Presence of Unmeasured Confounding. Journal of the American Statistical Association, 2022. DOI: 10.1080/01621459.2021.2023551
  2. Li X., Miao W., Lu F., Zhou XH. Improving efficiency of inference in clinical trials with external control data. Biometrics. 2021;1-10. DOI: 10.1111/biom.13583
  3. Qiu Y., Tao J., Zhou XH. Inference of Heterogeneous Treatment Effects Using Observational Data with High-Dimensional Covariates. Journal of the Royal Statistical Society Series B. 2021; 83:1016-1043.
  4. Li W., Geng Z., Zhou XH. Causal mediation analysis with sure outcomes of random events model. Statistics in Medicine, 2021.
    Guo W., Zhou XH., Ma S. Estimation of Optimal Individualized Treatment Rules Using a Covariate-Specific Treatment Effect Curve with High-Dimensional Covariates. Journal of American Statistical Association 2021, 116:533, 309-321.
  5. Huang Y. and Zhou XH. Identification of the optimal treatment regimen in the presence of missing covariates. Statistics in Medicine 2020; 20: 353-368
  6. Sheng E., Li W., and Zhou XH. Estimating causal effects of treatment in RCTs with provider and subject noncompliance. Statistics in Medicine 2019; 38: 735-750
  7. Wang L., Richardson T., Zhou XH. Causal analysis of ordinal treatments and binary outcomes under truncation by death. Journal of Royal Statistical Society Series B 2017; 79: 719-735
  8. Wang L., Zhou XH., and Richardson T. S. Identification and Estimation of Causal Effects with Outcomes Truncated by Death. Biometrika 2017;104: 597–612
  9. Li W., Zhou XH. Identifiability and Estimation of Causal Mediation Effects with Missing Data. Statistics in Medicine 2017; 36: 3948-3965.
  10. Wang X., Beste L.A., Maier M.M., and Zhou XH. Double robust estimator of average causal treatment effect for censored medical cost data. Statistics in Medicine 2016; 35: 3101-3116.
  11. Zheng C., Zhou XH. Causal mediation analysis in the multilevel intervention and multicomponent mediator case. Journal of Royal Statistical Society Series B (JRSS B) 2015; 77: 581-615.
  12. Wu Y., Zhao L., Hou Y., Li K., Zhou XH. Correcting for non-compliance in randomized non-inferiority trials with active and placebo control using structural models. Stat Med 2015; 34: 950-965.
  13. Ding P., Geng Z., Yan W., Zhou XH. Identifiability and Estimation of Causal Effects by Principal Stratification with Outcomes Truncated by Death. Journal of the American Statistical Association 2011; 106: 1578-1591.
  14. Taylor L., Zhou XH. Multiple Imputation Methods for Treatment Noncompliance and Nonresponse in Randomized Clinical Trials. Biometrics 2009; 65：88-95.
  15. Chen H., Geng Z., Zhou XH. Identifiability and estimation of causal effects in randomized trials with noncompliance and completely nonignorable missing data (with discussion). Biometrics 2009; 65：675-691.