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Epidemiology and Biostatistics Dissertation Defense: Shanika De Silva

Monday, December 5, 2022

10:30 AM-12:30 PM

Shanika De Silva, MS, will present "Propensity Score Methods for Spatial, Temporal, and Spatiotemporal Data."

Propensity score analyses allow researchers to estimate treatment, intervention, or exposure effects by mimicking the characteristics of a randomized controlled trial. However, traditional methods rely on strong assumptions that often do not hold for data collected over space and/or time. When these assumptions are violated, estimates of treatment effects will be biased. Of particular interest to this study is the assumption of “no unmeasured confounders”. In this dissertation, we investigate and present extensions of the traditional propensity score matching algorithm to accommodate spatial, temporal, and spatiotemporal data, respectively. We leverage the properties of spatially and temporally structured data to recover the unconfoundedness assumption within a Bayesian framework. We study the operating characteristics of the proposed models under different settings in a series of simulation studies. We observe that accounting for space or temporality when the observational data is spatial, temporal, or spatiotemporal can help recover the true treatment effect. Finally, we apply the proposed methods to examine the relationships between county-level exposure to PM2.5 and county-level preterm birth rates in the US between 2003 and 2006.

Committee Members: Dr. Leslie McClure (Chair), Dr. Félice Lê-Scherban, Dr. Nandita Mitra, Dr. Harrison Quick, Dr. Loni Tabb

For more information, please email

Contact Information

Nancy Colon-Anderson

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Nesbitt Hall, Room 719 and via Zoom


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