Inference in Spatial Experiments with Interference using the SpatialEffect Package

Cyrus Samii, Ye Wang, Jonathan Sullivan, P. M. Aronow

    Research output: Contribution to journalArticlepeer-review


    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.

    Original languageEnglish (US)
    Pages (from-to)138-156
    Number of pages19
    JournalJournal of Agricultural, Biological, and Environmental Statistics
    Issue number1
    StatePublished - Mar 2023


    • Causal inference
    • Experiments
    • Interference
    • Spatial statistics

    ASJC Scopus subject areas

    • Statistics and Probability
    • General Environmental Science
    • Agricultural and Biological Sciences (miscellaneous)
    • General Agricultural and Biological Sciences
    • Statistics, Probability and Uncertainty
    • Applied Mathematics


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