Sophie Woodward is a second-year Ph.D. student in Biostatistics at Harvard University investigating the intersection of spatial statistics and causal inference to inform data-driven decision-making. Under the guidance of Dr. Francesca Dominici, she is focused on developing methods to address spatial confounding in causal inference, as well as bridging common spatial regression modeling with causal inference methods.
Sophie joined the National Studies of Air Pollution and Health (NSAPH) in 2020 as an undergraduate researcher under the guidance of Dr. Xiao Wu and Dr. Danielle Braun. Her first two projects with the team investigated the association between air pollution exposure and COVID-19 mortality, employing Bayesian hierarchical modeling to correct ecological bias using data from the Census’s Public Use Microdata Sample. Sophie graduated from Harvard College with a Bachelor’s degree in Math and Statistics in 2022.
Sophie was awarded the National Science Foundation Graduate Research Fellowship in 2022 to pursue her PhD. Sophie’s dissertation research with Dr. Francesca Dominici focuses on causal inference methods for spatial data. A prevalent challenge is unmeasured spatial confounding, where an unobserved, spatially varying variable affects both exposure and outcome, leading to biased causal estimates and invalid confidence intervals. Sophie is developing a methodology to address spatial confounding bias restricted to certain spatial scales. She has also started a second project with Dr. Jose Zubizarreta, which explores the mechanisms by which common spatial random effect models implicitly generate a matched population and mirror key features of randomized experiments.