Inference in Spatial Experiments with Interference using the SpatialEffect Package

Cyrus Samii (), Ye Wang (), Jonathan Sullivan () and P. M. Aronow ()
Additional contact information
Cyrus Samii: New York University
Ye Wang: University of North Carolina
Jonathan Sullivan: University of Arizona
P. M. Aronow: Yale University

Journal of Agricultural, Biological and Environmental Statistics, 2023, vol. 28, issue 1, No 9, 138-156

Abstract: Abstract 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.

Keywords: Spatial statistics; Causal inference; Interference; Experiments (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s13253-022-00517-y

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