2011年于东南大学数学系获学士学位，2015年于香港中文大学统计系获博士学位，2016年加入中国人民大学统计与大数据研究院担任助理教授。研究领域涉及数理统计、计量经济学、随机分析，具体方向包括因果推断、缺失数据、污染数据、大样本理论、半参数模型的有效估计、非参数统计推断、随机微分方程、Malliavin随机分析等。

详见个人主页：https://sites.google.com/view/zheng-zhang/home

主要论文（按姓氏的英文首字母排序，*表示通讯作者，#表示指导的学生）

Causal Inference:

1. Chan K. C. G.*,Yam S. C. P.,and Zhang Z. (2016). Globally Efficient Nonparametric Inference of Average Treatment Effects by Empirical Balancing Calibration Weighting. Journal of the Royal Statistical Society: Series B . 78(3), 673-700. [Link]

2. Ai C., Huang L.# and Zhang Z.* (2020). A Mann-Whitney Test of Distributional Effects in A Multivalued Treatment. Journal of Statistical Planning and Inference. 209,85-100. [Link]

3. Chen X., Liu Y., Ma S., and Zhang Z.* (2020). Efficient Estimation of Treatment Effects using Neural Networks with A Diverging Number of Confounders. （Major Revision）. [Link]

4. Ai. C., Linton O., Motegi K. and Zhang Z.* (2021). A Unified Framework for Efficient Estimation of General Treatment Models.  Quantitative Economics. 12(3),779-816. [Link]

5. Ai. C., Linton O., and Zhang Z.* (2022). Estimation and Inference of Counterfactual Distribution and Quantile Functions in Continuous Treatment Models.  Journal of Econometrics. 228(1), 39-61.

6. Ai C., Huang L.# and Zhang Z.* (2022). A Simple and Efficient Estimation of Average Treatment Effects in Models with Unmeasured Confounders. Statistica Sinica. 32(3). [Link]

7. Huang W., Linton O., and Zhang Z.* (2022). A Unified Framework for Specification Tests of Continuous Treatment Effect Models.  Journal of Business & Economic Statistics. Accepted. [Link]

8. Huang W. and Zhang Z. *(2022). Nonparametric Estimation of Continuous Treatment Effect with Measurement Error. Journal of the Royal Statistical Society: Series B. (major revision).

9. Huang L.# and Zhang Z.* (2021). Nonparametric Estimation of General Mediation Effects by Calibration Weighting. Submitted.

Missing Data:

1. Hamori S., Motegi K.* and Zhang Z. (2019). Calibration Estimation for Semiparametric Copula Models with Data Missing at Random. Journal of Multivariate Analysis . 173, 85-109. [Link]

2. Hamori S., Motegi K. and Zhang Z.* (2020). Copula-based Regression Models with Responses Missing at Random. Journal of Multivariate Analysis. 180, #104654. [Link]

3.Ai C., Linton O. and Zhang Z.* (2020). A Simple and Efficient Estimation Method for Models with Nonignorable Missing Data. Statistica Sinica . 30, 1949-1970. [Link]

Semi/Nonparametric Statistics:

1. Ai. C., Sun L., Zhang Z.* and Zhu L. (2019). Testing Independence and Conditional Independence via Mutual Information. (Minor Revision at Journal of Econometrics).

Stochastic Analysis:

1. Bensoussan A., Yam S. C. P.*, and Zhang Z. (2015). Well-posedness of Mean-field Type Forward-backward Stochastic Differential Equations. Stochastic Processes and their Applications . 125(9), 3327-3354. [Link]

2. Wright J. A.*, Yam S. C. P., and Zhang Z. (2017). Enlargement of Filtration on Poisson Space: A Malliavin Calculus Approach. Stochastics . 90(5), 682-700. [Link]

3. Privault N., Yam S. C. P.*, and Zhang Z. (2019). Poisson Discretizations of Wiener Functionals and Malliavin Operators with Wasserstein Estimates. Stochastic Processes and their Applications . 129(9), 3376-3405. [Link]

数理统计、计量经济学、随机分析