Research interests

• Causal inference

• Conditional independence testing/variable selection

Statistics (* equal contribution)

• A general condition for bias attenuation by a nondifferentially mismeasured confounder
  Jeffrey Zhang and Junu Lee
  Preprint.
  Paper

• On Identification of Dynamic Treatment Regimes with Proxies of Hidden Confounders
  Jeffrey Zhang and Eric Tchetgen Tchetgen
  Preprint.
  Paper

• Bridging the Gap Between Design and Analysis: Randomization Inference and Sensitivity Analysis for Matched Observational Studies with Treatment Doses
  Jeffrey Zhang and Siyu Heng
  Preprint.
  Paper

• Doubly robust and computationally efficient high-dimensional variable selection
  Abhinav Chakraborty*, Jeffrey Zhang*, and Eugene Katsevich
  Preprint.
  Paper Code

• Sensitivity analysis for matched observational studies with continuous exposures and binary outcomes.
  Jeffrey Zhang, Dylan Small, and Siyu Heng
  Biometrika, 2024.
  Paper CRAN Code

• Does matching introduce confounding or selection bias into the matched case-control design?
  Fei Wan, Siobhan Sutcliffe, Jeffrey Zhang, and Dylan Small
  Observational Studies, 10:1-9, 2024.
  Paper

• Sensitivity Analysis for Observational Studies with Recurrent Events
  Jeffrey Zhang and Dylan Small
  Lifetime Data Analysis, 30:237–261, 2024.
  Paper

• Proximal causal inference without uniqueness assumptions
  Jeffrey Zhang and Eric Tchetgen Tchetgen
  Statistics and Probability Letters, 198, 2023.
  Paper

Collaborative

• MRI-Based Brain Volume Scoring in Cerebral Malaria Is Externally Valid and Applicable to Lower-Resolution Images.
  M. S. Goyal, L. Vidal, K. Chetcuti, C. Chilingulo, K. Ibrahim, J Zhang, D. Small, K. B Seydel, N. O’Brien, T. E. Taylor, D. G. Postels
  American Journal of Neuroradiology, 2:205-210, 2024.
  Paper

Comments

• A method to aid statistical judgment on outliers: Comment on Hill’s The Statistician in Medicine.
  Jeffrey Zhang, Bo Zhang, and Dylan Small
  Statistics in Medicine, 40:58–63, 2021.
  Paper