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

    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

    • Causal inference
    • Experiments
    • Interference
    • Spatial statistics

    ASJC Scopus subject areas

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

    Fingerprint

    Dive into the research topics of 'Inference in Spatial Experiments with Interference using the SpatialEffect Package'. Together they form a unique fingerprint.

    Cite this