Published papers
18. Jiang, Z. and Ding, P. (accepted) Identification of Causal Effects Within Principal Strata Using Auxiliary Variables. Statistical Science [arXiv]
17. Imai, K., Jiang, Z. and Malani, A. (accepted) Causal inference with interference and noncompliance in the two-stage randomized experiments. Journal of the American Statistical Association [link]
16. Imai, K. and Jiang, Z. (accepted) Identification and sensitivity analysis of contagious effect with placebo-controlled randomized experiments. Journal of the Royal Statistical Society: Series A (Statistics in Society) [link][preprint]
15. Jiang, Z. and Ding, P. (2020) Measurement errors in the binary instrumental variable model. Biometrika [link][arXiv]
14. Jiang, Z. and VanderWeele, T. J. (2019) Causal mediation analysis in the presence of a misclassified binary exposure. Epidemiologic Methods
13. Imai, K. and Jiang, Z. (2019) Discussion of ``The Blessings of Multiple Causes'' by Wang and Blei. Journal of the American Statistical Association [link][arXiv]
12. Imai, K. and Jiang, Z. (2018) A sensitivity analysis for missing outcomes due to truncation-by-death under the matched-pairs design. Statistics in Medicine [link][preprint]
11. Jiang, Z. and Ding, P. (2018) Using missing types to improve partial identification with application to a study of HIV prevalence in Malawi. Annals of Applied Statistics [link][arXiv]
10. Li, W., Jiang, Z., Geng, Z. and Zhou, XH. (2018) Identification of causal effects in the presence of measurement error and latent confounding. Biometrical Journal [link]
9. Jiang, Z. and Ding, P. (2017) The Directions of Selection Bias. Statistics and Probability Letters [link][arXiv]
8. Jiang, Z. and Ding, P. (2016) Robust modeling using non-elliptically contoured multivariate t distributions. Journal of Statistical Planning and Inference [link]
7. Jiang, Z., Ding, P. and Geng, Z. (2016) Principal causal effect identification and surrogate endpoint evaluation by multiple trials. Journal of the Royal Statistical Society: Series B (Statistical Methodology) [link] [arXiv]
6. Jiang, Z. and VanderWeele, T. J. (2015) When is the difference method conservative for mediation? (With discussion) American Journal of Epidemiology [link]
5. Jiang, Z., Ding, P. and Geng, Z. (2015) Qualitative evaluation of associations by the transitivity of the association signs. Statistica Sinica [link]
4. Jiang, Z. and VanderWeele, T. J. (2015) Causal mediation analysis in the presence of a mismeasured outcome. Epidemiology [link]
3. VanderWeele, T. J., Jiang, Z. and Chiba, Y. (2014) Monotone confounding, monotone treatment selection, and monotone treatment response. Journal of Causal Inference [link]
2. Jiang, Z., VanderWeele T. J. (2014) Additive interaction in the presence of a mismeasured outcome. American Journal of Epidemiology [link]
1. Liu, H., Jiang, Z., Fang, X., Fu, H., Zheng, X., Cha, L. and Li, W. (2012) Generate gene expression profile from high-throughput sequencing data. Frontiers of Mathematics in China [link]
18. Jiang, Z. and Ding, P. (accepted) Identification of Causal Effects Within Principal Strata Using Auxiliary Variables. Statistical Science [arXiv]
17. Imai, K., Jiang, Z. and Malani, A. (accepted) Causal inference with interference and noncompliance in the two-stage randomized experiments. Journal of the American Statistical Association [link]
16. Imai, K. and Jiang, Z. (accepted) Identification and sensitivity analysis of contagious effect with placebo-controlled randomized experiments. Journal of the Royal Statistical Society: Series A (Statistics in Society) [link][preprint]
15. Jiang, Z. and Ding, P. (2020) Measurement errors in the binary instrumental variable model. Biometrika [link][arXiv]
14. Jiang, Z. and VanderWeele, T. J. (2019) Causal mediation analysis in the presence of a misclassified binary exposure. Epidemiologic Methods
13. Imai, K. and Jiang, Z. (2019) Discussion of ``The Blessings of Multiple Causes'' by Wang and Blei. Journal of the American Statistical Association [link][arXiv]
12. Imai, K. and Jiang, Z. (2018) A sensitivity analysis for missing outcomes due to truncation-by-death under the matched-pairs design. Statistics in Medicine [link][preprint]
11. Jiang, Z. and Ding, P. (2018) Using missing types to improve partial identification with application to a study of HIV prevalence in Malawi. Annals of Applied Statistics [link][arXiv]
10. Li, W., Jiang, Z., Geng, Z. and Zhou, XH. (2018) Identification of causal effects in the presence of measurement error and latent confounding. Biometrical Journal [link]
9. Jiang, Z. and Ding, P. (2017) The Directions of Selection Bias. Statistics and Probability Letters [link][arXiv]
8. Jiang, Z. and Ding, P. (2016) Robust modeling using non-elliptically contoured multivariate t distributions. Journal of Statistical Planning and Inference [link]
7. Jiang, Z., Ding, P. and Geng, Z. (2016) Principal causal effect identification and surrogate endpoint evaluation by multiple trials. Journal of the Royal Statistical Society: Series B (Statistical Methodology) [link] [arXiv]
6. Jiang, Z. and VanderWeele, T. J. (2015) When is the difference method conservative for mediation? (With discussion) American Journal of Epidemiology [link]
5. Jiang, Z., Ding, P. and Geng, Z. (2015) Qualitative evaluation of associations by the transitivity of the association signs. Statistica Sinica [link]
4. Jiang, Z. and VanderWeele, T. J. (2015) Causal mediation analysis in the presence of a mismeasured outcome. Epidemiology [link]
3. VanderWeele, T. J., Jiang, Z. and Chiba, Y. (2014) Monotone confounding, monotone treatment selection, and monotone treatment response. Journal of Causal Inference [link]
2. Jiang, Z., VanderWeele T. J. (2014) Additive interaction in the presence of a mismeasured outcome. American Journal of Epidemiology [link]
1. Liu, H., Jiang, Z., Fang, X., Fu, H., Zheng, X., Cha, L. and Li, W. (2012) Generate gene expression profile from high-throughput sequencing data. Frontiers of Mathematics in China [link]
