1. Multiply robust estimation of causal effects under principal ignorability
    Jiang, Z., Yang, S. and Ding, P.
    Journal of the Royal Statistical Society: Series B (Statistical Methodology), accepted, 2022 [arXiv]
  2. Experimental evaluation of algorithm-assisted human decision-making: application to pretrial public safety assessment (with discussion)
    Imai, K., Jiang, Z., Greiner, J., Halen, R. and Shin, S.
    Journal of the Royal Statistical Society: Series A (Statistics in Society), accepted, 2021 [arXiv]
  3. Identification of causal effects within principal strata using auxiliary variables
    Jiang, Z. and Ding, P.
    Statistical Science, 36, 493-508, 2021 [arXiv]
  4. Causal inference with interference and noncompliance in the two-stage randomized experiments
    Imai, K., Jiang, Z.* and Malani, A.
    Journal of the American Statistical Association, 116, 632-644, 2021 [DOI]
  5. Measurement errors in the binary instrumental variable model
    Jiang, Z. and Ding, P.
    Biometrika, 107, 238-245, 2020 [DOI]
  6. Identification and sensitivity analysis of contagion effects with randomized placebo-controlled trials
    Imai, K. and Jiang, Z.*
    Journal of the Royal Statistical Society: Series A (Statistics in Society), 183, 1637-1657, 2020 [DOI]
  7. Comment: the challenges of multiple causes
    Imai, K. and Jiang, Z.
    Journal of the American Statistical Association, 114, 1605-1610, 2020 [DOI]
  8. Causal mediation analysis in the presence of a misclassified binary exposure
    Jiang, Z. and VanderWeele, T. J.
    Epidemiologic Methods, 2019 [DOI]
  9. A sensitivity analysis for missing outcomes due to truncation-by-death under the matched-pairs design
    Imai, K., and Jiang, Z.*
    Statistics in Medicine, 37, 2907-2922, 2018 [DOI]
  10. Using missing types to improve partial identification with application to a study of HIV prevalence in Malawi
    Jiang, Z. and Ding, P.
    Annals of Applied Statistics, 12, 1831-1852, 2018 [DOI]
  11. Identification of causal effects with latent confounding and classical additive errors in treatment
    Li, W., Jiang, Z., Geng, Z. and Zhou, XH.
    Biometrical Journal, 60, 498-515, 2018 [DOI]
  12. The Directions of Selection Bias
    Jiang, Z. and Ding, P.
    Statistics and Probability Letters, 125, 104-109, 2017 [DOI]
  13. Robust modeling using non-elliptically contoured multivariate $t$ distributions
    Jiang, Z. and Ding, P.
    Journal of Statistical Planning and Inference, 177, 50-63, 2016 [DOI]
  14. Principal causal effect identification and surrogate endpoint evaluation by multiple trials
    Jiang, Z., Ding, P. and Geng, Z.
    Journal of the Royal Statistical Society: Series B (Statistical Methodology), 78, 829-848, 2016 [DOI]
  15. When is the difference method conservative for mediation? (with discussion)
    Bounds or sensitivity analysis? Which to prefer for mediation? (rejoinder to discussion)
    Jiang, Z. and VanderWeele, T. J.
    American Journal of Epidemiology, 182, 105–117, 2015 [DOI] [DOI:rejoinder]
  16. Qualitative evaluation of associations by the transitivity of the association signs
    Jiang, Z., Ding, P. and Geng, Z.
    Statistica Sinica, 25, 1065–1079, 2015 [DOI]
  17. Causal mediation analysis in the presence of a mismeasured outcome
    Jiang, Z. and VanderWeele, T. J.
    Epidemiology, 26, e8-e9, 2015 [DOI]
  18. Additive interaction in the presence of a mismeasured outcome
    Jiang, Z. and VanderWeele, T. J.
    American Journal of Epidemiology, 181, 81-82, 2015 [DOI]
  19. Monotone confounding, monotone treatment selection, and monotone treatment response
    Jiang, Z., Chiba, Y. and VanderWeele, T. J.
    Journal of Causal Inference, 2, 1-12, 2015 [DOI]