Jessica Young

Assistant Professor in the Department of Epidemiology

Department of Epidemiology

I am an Assistant Professor in the Department of Population Medicine at the Harvard Medical School and the Department of Epidemiology at the Harvard Chan School of Public Health.  My research focuses on the development and application of statistical methods for making valid causal inferences in longitudinal studies with complications such as time-varying confounding, competing events and censoring.   I am a Co-Investigator and Biostatistician for the Project Viva Cohort.

Editorial Positions:

  • Member of the Editorial Board Epidemiology
  • Associate Editor Biometrics

Selected Publications:

  1. Chiu Y, Rifas-Shiman SL, Kleinman K, Oken E, Young JG. Effects of intergenerational exposure interventions on adolescent outcomes: an application of inverse probability weighting to longitudinal pre-birth cohort data. Paediatric and Perinatal Epidemiology. 2020 (In press)
  2. Young JG, Stensrud MJ, Tchetgen Tchetgen EJ, Hernán MA. A causal framework for classical statistical estimands in failure time settings with competing events. Statistics in Medicine. 2020; 15;39(8):1199-1236.
  3. Young JG, Logan RW, Robins JM and Hernán MA.  Inverse probability weighted estimation of risk under representative interventions in observational studies.  Journal of the American Statistical Association. 2019; 114(526):938-947.
  4. Young JG, Vatsa R, Murrary EJ, Hernán MA.  Interval-cohort designs and bias in the estimation of per-protocol effects: a simulation study. Trials. 2019; 20(1): 552.
  5. Oken E. Aris IM, Young JG.  Pre-pregnancy weight and preterm birth: a causal relation? Lancet Diabetes and Endocrinology. 2019; 7(9):663-665.
  6. Young JG, Hernán MA, and Robins JM.  Identification, estimation and approximation of risk under interventions that depend on the natural value of treatment using observational data. Epidemiologic Methods. 2014; 3(1): 1-19.  PMCID: PMC4387917
  7. Young JG and Tchetgen Tchetgen EJ.  Simulation from a known Cox MSM using standard parametric models for the g-formula. Statistics in Medicine. 2014; 33(6):1001-14.  PMCID: PMC3947915
  8. Young JG, Hernán MA, Picciotto S, and Robins JM. Relation between three classes of structural models for the effect of a time-varying exposure on survival. Lifetime Data Analysis. 2010;16(1):71-84.  PMCID: PMC3635680