Zheng Zhang (张政) 's Website

Publications (alphabetical order. * indicates the corresponding author. # represents supervised students)

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.

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

3. Wright J. A.*, Yam S. C. P., and Zhang Z. (2018). Enlargement of Filtration on Poisson Space: A Malliavin Calculus Approach. Stochastics. 90(5), 682-700.

4. 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.

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

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

7. Ai C., Linton O. and Zhang Z.* (2020). A Simple and Efficient Estimation Method for Models with Non-ignorable Missing Data. Statistica Sinica. 30, 1949-1970. [Supplemental Material]

8. 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. [Supplemental Material]

9. 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.

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

11. 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).

12. Huang W., Linton O., and Zhang Z.* (2022). A Unified Framework for Specification Tests of Continuous Treatment Effect Models. Journal of Business & Economic Statistics. 40(4), 1817-1830.

13. Huang W. and Zhang Z.* (2023). Nonparametric Estimation of Continuous Treatment Effect with Measurement Error. Journal of the Royal Statistical Society: Series B. 85(2), 474–496.

14. Ai. C., Sun L., Zhang Z.* and Zhu L. (2024). Testing Unconditional and Conditional Independence via Mutual Information. Journal of Econometrics. 240(2), 105335.

15. Chen X., Liu Y., Ma S*. and Zhang Z*. (2024). Causal Inference of General Treatment Effects Using Neural Networks with A Diverging Number of Confounders. Journal of Econometrics. 238(1).

16. Huang L.# Huang W., Linton O, and Zhang Z.* (2024). Nonparametric Estimation of Mediation Effects with A General Treatment. Econometric Reviews. 43(2-4).

17. Xiao F., Ji C., Zhang Z., and Wang R.* (2025). Decouple-and-Couple Learning in Multi-modal Brain Tumor Segmentation. IEEE Journal of Biomedical and Health Informatics.

18. Hou H.#, Huang W., and Zhang Z.* (2025). Non-parametric Quantile Regression and Uniform Inference with Unknown Error Distribution. Journal of Business & Economic Statistics. 1-23.

19. Wang M.#, Huang W., Gong M., and Zhang Z.* (2025). Projection Pursuit Based Density Ratio Estimation. International Conference on Machine Learning. Accepted.

20. Tan R., Huang W.*, Zhang Z.*, and Yin G. (2025). Causal Effect of Functional Treatment. Journal of Machine Learning Research. 26 (91), 1-39.

Submitted Papers

1. Ai C., Huang W. and Zhang Z.* (2025+). Adaptive Uniform Nonparametric Inference for General Continuous Treatment Models.

2. Jiang S., Li R., Ma S.*, and Zhang. Z* (2025+). Causal Inference on Quantile Dose-response Functions via Local ReLU Least Squares Weighting.

3. Huang W., Ma S., Zhang Y.#. and Zhang Z.* (2025+). Causal Inference for Dose-response Functions on Treatment Intervals via Semiparametric Minimum Variance Weighting.

4. Ai. C., Xu Z.#, and Zhang Z.* (2025+). Testing Conditional Independence via Density Ratio Regression.

5. Wu Z.#, Wang M.#, Huang W., and Zhang Z.* (2025+). A New and Efficient Debiased Estimation of General Treatment Models by Balanced Neural Networks Weighting.

6. Zhang Y.#, Lin L., and Zhang Z. (2025+) Higher-Order Debiased Estimators for General Treatment Models.

Working Papers

1. Chan K. C. G., Imai K., Yam S. C. P., and Zhang Z. (2016). Efficient Nonparametric Estimation of Causal Mediation Effects.

2. Sheu S., Sun L., and Zhang Z. (2018). Portfolio Optimization with Delay Factor Models.

3. Ing C. K., Pan Y. S., and Zhang Z. (2024+). Multiple Robust Estimation of Average Treatment Effects with High-Dimensional Covariates.

4. Huang W., Zhang Y.#. and Zhang Z. (2024+). Testing Monotonicity of Continuous Treatment Effects and Shape Constrained Estimation.

5. Huang W., Xu Z.#, and Zhang Z. (2024+). Non-parametric Efficient Estimation of (Conditional) Mutual Information.

6. Hou H.#, Huang W, and Zhang Z. (2024+). Uniform Non-parametric Inference for Conditional Average Treatment Effects with Measurement Error.

7. Wu Z.# and Zhang Z. (2024+). Non-parametric Estimation and Uniform Inference for Quantile Dose-response Model via Double Empirical Likelihood Learning.

8. Ai C., Wu Z.# and Zhang Z. (2025+). Data-Automated Policy Learning under Nonlinear Welfare.

Research Grants

Grants as Principal Investigator

1. National High-Level Talent Special Support Programs (10,000 Talents Program)- Outstanding Young Talents, 2024-2027.

2. NSFC the General Program. Grant Number 12371284. 2024-2027. "因果分析中连续处理效应的非参数建模与统计推断" (Nonparametric Modeling and Inference for Continuous Treatment Effect).

3. NSFC the Young Scientists Program. Grant Number 12001535. 2021-2023. "复杂数据下因果关系有效推断的理论与方法" (Efficient Causal Inference from Complex Data: Theory and Method).

4. Natural Science Foundation of Beijing (General Program). Grant Number 1222007. 2022-2024. "连续处理效应的建模与统计推断" (Modeling and Inference for Continuous Treatment Effect).

5. Research Grants of RUC（General Program: 23XNA025). 2023.04.03-2025.12.13. ”测量误差数据下因果推断的方法与理论研究“.

6. Research Grant of RUC. (Science and Technology Progam: 17XNLF07). 2017-2018. "Nonparametric Methods for Missing Data Problems".

7. Research Grant for International Collaboration, RUC. Grant Number KYGJC2022014. 2022-2023. “Modeling and Inference for Continuous Treatment Effect”.

8. Research Grant for International Collaboration, RUC. Grant Number KYGJC2023011. 2023-2024. "广义连续处理效应的非参数建模与统计推断".

9. Research Grant of Center for Applied Statistics, RUC. 2022-2023. "Modeling and Inference for Treatment Effect with Contaminated Data".

10. Start-up Research Grant, RUC. 2016-2019.

Grants as Co-Investigator

1. National Key R&D Program of China. Grant Number 2022YFA1008300. 2022.12-2027.11. “面向跨尺度医学数据的融合表征及因果数学理论与应用”. (with Ruxin Wang et al.)

2. National Key R&D Program of China. Grant Number 2023YFA1008700. 2024.1-2028.12 "非独立同分布大数据统计基础理论、方法与应用". (with Liping Zhu et al.)

3. NSFC the General Program. Grant Number 2017010151. 2018-2021. “高维函数型数据的估计与推断方法研究”. (with Shaojun Guo et al.)

4. NSFC the General Program. Grant Number 12171476. 2022-2025. ``自适应随机化临床试验的设计与分析". (with Wei Ma et al.)

5. NSFC the Young Scientists Program. Grant Number 11801559. 2019-2021. ``协变量自适应试验设计及统计推断". (with Wei Ma et al.)

6. NSFC the Young Scientists Program. Grant Number 11501261. 2016-2018. ``Structural Equation Modeling with Non-normal and Missing Data". (with Pengfei Liu)