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因果推断

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因果推断

       周教授在研究因果作用的可识别性、估计量的稳健性、被死亡截断结局的因果推断、随机激励设计的因果推断、精准治疗等多个研究上取得了突出成果。其中，周教授提出使用工具变量方法将意向治疗效果与真实治疗效果联系起来，使用贝叶斯方法进行推断和灵敏性分析，提出了一系列相关因果效应估计的新方法和理论。此外，在精准医疗方面，周教授首先提出使用生物标志物调节治疗效果曲线（BATE）和协变量特征治疗效果曲线（CSTE）来表示给定生物标志物水平下的条件平均治疗效果，为患者选择最佳治疗方案，并严格证明了提出的新统计方法的数学性质。随后,周教授进一步提出了结局变量为二值的CSTE曲线及其置信带方法，并把CSTE曲线推广到高维协变量场景，完善了其数学理论。

部分发表文章：
  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.