B0594
Title: SMaC: Spatial matrix completion method
Authors: Giulio Grossi - University of Florence (Italy) [presenting]
Alessandra Mattei - University of Florence (Italy)
Georgia Papadogeorgou - University of Florida (United States)
Abstract: Synthetic control methods are commonly used in panel data settings to evaluate the effect of an intervention. In many of these cases, the treated and control time series correspond to spatial areas such as regions or neighbourhoods. The work in the setting where a treatment is applied at a given location and its effect can emanate across space. Then, an area of a certain size around the intervention point is considered to be the treated area. Synthetic control methods can be used to evaluate the effect that the treatment had in the treated area, but it is often unclear how far the treatment's effect propagates. Therefore, researchers might consider treated areas of different sizes and apply synthetic control methods separately for each one of them. However, this approach ignores the spatial structure of the data and can lead to efficiency loss in spatial settings. The proposal is to deal with these issues by developing a Bayesian spatial matrix completion framework that allows predicting the missing potential outcomes in the different areas around the intervention point while accounting for the spatial structure of the data. Specifically, the missing time series in the absence of treatment for the treated areas of all sizes are imputed using a weighted average of control time series, where the weights are assumed to vary smoothly over space according to a Gaussian process.
