Event Description
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 nanderson@drexel.edu
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