Samii, C., Wang, Y., Sullivan, J. et al. Inference in Spatial Experiments with Interference using the SpatialEffect Package. JABES 28, 138–156 (2023). https://doi.org/10.1007/s13253-022-00517-y
This paper presents methods for analyzing spatial experiments when complex spillovers, displacement effects, and other types of “interference” are present. We present a robust, design-based approach to analyzing effects in such settings. The design-based approach derives inferential properties for causal effect estimators from known features of the experimental design, in a manner analogous to inference in sample surveys. The methods presented here target a quantity of interest called the “average marginalized response,” which is equal to the average effect of activating a treatment at an intervention node that is a given distance away, averaging ambient effects emanating from other intervention nodes. We provide a step-by-step tutorial based on the SpatialEffect package for R. We apply the methods to a randomized experiment on payments for community forest conservation in Uganda, showing how our methods reveal possibly substantial spatial spillovers that more conventional analyses cannot detect.
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