B.S., B.A., Peking University, 2011; Ph.D., Peking University, 2016; Postdoctoral Research Associate, Princeton University, 2016-18; Postdoctoral Fellow, Harvard University, 2018-19.
Area(s) of Specialization:
Causal inference, missing data, measurement error, statistical inference, Bayesian statistics
My main interest is in causal inference including:
- Measurement error and misclassification in causal inference
- Principal stratification: noncompliance, surrogate, truncation by death
- Causal mechanisms: mediation analysis, interaction, interference
- Instrumental variable approaches: latent confounders, identifiability
- Randomization-based analysis of experiments
- Interference: spillover effect, contagious effect and infectiousness effect
I am also interested in missing data problems under non-ignorable missing data mechanisms:
- Identification and partial identification
Measurement errors in the binary instrumental variable model, Jiang, Z. and Ding, P. (In press) Biometrika.
Using missing types to improve partial identification with application to a study of HIV prevalence in Malawi, Jiang, Z. and Ding, P. (2018) Annals of Applied Statistics, 12, 1831–1852.
Principal causal effect identification and surrogate endpoint evaluation by multiple trials, Jiang, Z., Ding, P. and Geng, Z. (2016) Journal of the Royal Statistical Society: Series B (Statistical Methodology), 78, 829–848.
When is the difference method conservative for mediation? (With discussion), Jiang, Z. and VanderWeele, T. J. (2015) American Journal of Epidemiology, 182, 105–108.