Working papers
Li, Y., Jiang, Z. and Geng, Z. Using twin study data to identify causal effects under latent confounding.
Imai, K., Jiang, Z. and Malani, A. Causal inference with interference and noncompliance in the two-stage randomized experiments. Submitted
Jiang, Z. and Ding, P. Identifiability of causal effects within principal strata using an auxiliary variable. Submitted
Chen, S. , Jiang, Z. and Imai, K. Estimating complier average causal effects from multiple data sources
Imai, K. and Jiang, Z. Identification and sensitivity analysis of contagious effect with placebo-controlled randomized experiments. Submitted [preprint]
Jiang, Z. and VanderWeele, T. J. Causal mediation analysis in the presence of a misclassified binary exposure. Submitted
Published papers
13. Jiang, Z. and Ding, P. (accepted) Measurement errors in the binary instrumental variable model. Biometrika
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]
Li, Y., Jiang, Z. and Geng, Z. Using twin study data to identify causal effects under latent confounding.
Imai, K., Jiang, Z. and Malani, A. Causal inference with interference and noncompliance in the two-stage randomized experiments. Submitted
Jiang, Z. and Ding, P. Identifiability of causal effects within principal strata using an auxiliary variable. Submitted
Chen, S. , Jiang, Z. and Imai, K. Estimating complier average causal effects from multiple data sources
Imai, K. and Jiang, Z. Identification and sensitivity analysis of contagious effect with placebo-controlled randomized experiments. Submitted [preprint]
Jiang, Z. and VanderWeele, T. J. Causal mediation analysis in the presence of a misclassified binary exposure. Submitted
Published papers
13. Jiang, Z. and Ding, P. (accepted) Measurement errors in the binary instrumental variable model. Biometrika
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]
