Learning about the impact of localized events taking place over time is a multi-faceted problem, as it requires taking into account the influence of multiple dimensions, including: geographical location, timing, and attributes of events. In this talk, Levin argues that traditional regression approaches-which assume the existence of a linear, or otherwise known, relationship between predictors and outcomes-are inappropriate for learning about the impact of spatial and temporal proximity to events. Instead, Levin proposes using regression trees, an approach that allows addressing the problem in a non-parametric and efficient manner. Levin illustrates the usefulness of the proposed procedure by studying the impact of mass shootings on opinions about gun control and of distance to the border (and immigrant detention facilities) on support for immigration reform.

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