The Lecture Committee is delighted to announce our first speaker for the inaugural David O. Siegmund Lecture. This special series of seminars was named for Professor Emeritus Siegmund to honor his accomplished career in methodological and applied statistical research, as well as his dedication as a mentor in his field. The Committee is excited to recognize and highlight these same merits in their selection for 2025:
Dr. Brian Reich is the Gertrude M. Cox Distinguished Professor of Statistics at North Carolina State University. His research interests include Bayesian methods, spatial statistics, extreme value analysis, variable selection and dimension reduction and machine learning. In addition to these methodological interests, Brian applies these methods to environmental areas such as ecology, epidemiology, meteorology, and climate. His work is funded by grants from the NIH, NSF and the NSA and he currently serves as Director of the NSF training grant “Uncertainty Quantification for the Life Sciences” and co-director of the NSF REU “Directed Research for Undergraduates in Mathematics and Statistics”. For this body of work Dr. Reich has won numerous awards including the American Statistical Association Outstanding Statistical Application Award and the LeRoy & Elva Martin Teaching Award at NC State.
Day 2: The blessing of spatial dependence: Adjusting for unmeasured confounders in environmental health studies
Confounding complicates exposure effect estimation in environmental health studies. Causal inference is largely concerned with properly adjusting for measured confounding variables, but adjusting for an unmeasured confounder is generally an intractable problem. However, in the spatial setting it may be possible under certain conditions. We propose a model in the spectral domain to allow for different degrees of confounding at different spatial resolutions. The key assumption that ensures identifiability is that confounding present at global scales dissipates at local scales. We derive necessary conditions on the coherence between the treatment variable of interest and the unmeasured confounder that ensure the causal effect of the treatment is estimable. The problem of unmeasured confounders is exacerbated in studies with multiple health outcomes and environmental exposure variables, as the source and magnitude of confounding bias may differ across exposure/outcome pairs. Our model for the exposure effects is a three-way tensor over exposure, outcome, and spatial scale. We use a canonical polyadic decomposition and shrinkage priors to encourage sparsity and borrow strength across the dimensions of the tensor. We demonstrate the performance of our method in an extensive simulation study and data analysis to understand the relationship between disaster resilience and the incidence of chronic diseases.
The David O. Siegmund Lecture Committee for 2025 are:
- Hua Tang, Professor of Genetics, Stanford University
- Paul Switzer, Professor Emeritus of Statistics, Stanford University
- Nancy Zhang, Professor of Statistics and Data Science, Wharton School University of Pennsylvania