Sean McGrath develops statistical methods for drawing more reliable conclusions about cause and effect and for combining evidence across studies and populations. He also applies causal inference and data integration methods to answer substantive questions in public health and biomedical sciences, especially in infectious disease epidemiology and oncology.
McGrath’s research in causal inference focuses on estimating the effects of treatments strategies that vary over time and on methods that can incorporate flexible statistical and machine learning models. His work in data integration focuses on methods for meta-analysis, especially when studies have incomplete and heterogeneous reporting of outcomes. He also develops open-source statistical software that allows researchers to apply these methods.
McGrath is also an assistant professor in the Department of Statistics and Data Science in Arts & Sciences. Before joining WashU, he was a postdoctoral researcher in the Department of Biostatistics at the Yale School of Public Health and in the Department of Population Medicine at Harvard Medical School and Harvard Pilgrim Health Care Institute. He earned his PhD in biostatistics from Harvard University and his bachelor’s degree in mathematics from McGill University.
Areas of Focus:
- Causal inference
- Meta-analysis and evidence synthesis
- Data integration
- Statistical software
Featured Publications
- gfoRmula: An R package for estimating the effects of sustained treatment strategies via the parametric g-formula
Patterns
June 2020 - Wildfire exposure and academic performance in Brazil: A causal inference approach for spatiotemporal data
Science of the Total Environment
December 2023 - Meta-analysis of median survival times with inverse-variance weighting
Statistics in Medicine
April 2026 - Estimating the sample mean and standard deviation from commonly reported quantiles in meta-analysis
Statistical Methods in Medical Research
January 2020