I’m an incoming assistant professor in the Department of Statistics and Data Science and the School of Public Health at Washington University in St. Louis. My research focuses on developing and applying statistical methods for causal inference and data integration (meta-analysis, transfer learning, transportability). I also actively work on developing open-source statistical software in these domains.
I’m currently a postdoctoral researcher in the Department of Biostatistics at Yale School of Public Health. Previously, I was a postdoctoral researcher in the Department of Population Medicine at Harvard Medical School and Harvard Pilgrim Health Care Institute. I received my Ph.D. in Biostatistics from Harvard University, advised by Rajarshi Mukherjee. Prior to that, I completed a B.Sc. in Mathematics at McGill University.
Selected Papers
- McGrath S, Mukherjee R. Nuisance function tuning and sample splitting for optimally estimating a doubly robust functional. arXiv (pre-print).
- McGrath S, Mukherjee D, Mukherjee R, Wang ZJ. Optimal nuisance function tuning for estimating a doubly robust functional under proportional asymptotics. Advances in Neural Information Processing Systems (NeurIPS). Spotlight paper. 2025; 38: 1-73.
- McGrath S, Kawahara T, Petimar J, Rifas-Shiman SL, Díaz I, Block JP, Young JG. Time-smoothed inverse probability weighted estimation of effects of generalized time-varying treatment strategies on repeated outcomes truncated by death. arXiv (pre-print).
- McGrath S, Lin V, Zhang Z, Petito LC, Logan RW, Hernán MA, Young JG. gfoRmula: An R package for estimating the effects of sustained treatment strategies via the parametric g-formula. Patterns. 2020; 1: 100008.
- McGrath S, Zhu C, O’Dea R, Guo M, Duan R. LEARNER: A transfer learning method for low-rank matrix estimation. arXiv (pre-print).
- McGrath S, Yang CH, Kimmelman J, Ozturk O, Steele R, Benedetti A. Meta-analysis of median survival times with inverse-variance weighting. Statistics in Medicine. In press.
See the Papers page for a broader list.