Dr. Young is an Assistant Professor and Biostatistician in the Department of Population Medicine. Her research focuses on the development and application of statistical methods that may remain valid for estimating the causal effects of time-varying treatment strategies on health outcomes in the face of complex time-varying confounding and selection bias. She has particular interest in failure event outcomes that may be subject to competing risk events and dynamic time-varying treatment strategies; i.e. strategies under which treatment assignment at a given time may depend on time-evolving patient characteristics.
Dr. Young received her doctoral degree in Biostatistics from the University of California, Berkeley in 2007. Prior to joining DPM, she was a Postdoctoral Research Fellow and Research Associate in the Program on Causal Inference at the Harvard T.H. Chan School of Public Health.
Young JG, Tchetgen Tchetgen E, Hernan MA (2018). The choice to define competing risk events as censoring events and implications for causal inference. arXiv:1806.06136
Young JG, Stensrud MJ, Didelez V, Robins JM, Hernan MA (2019). Separable Effects for Causal Inference in the Presence of Competing Risks. arXiv:1901.09472