Inference in Spatial Experiments with Interference using the SpatialEffect Package

TL;DR

This paper introduces a robust, design-based method for analyzing spatial experiments with interference, using the SpatialEffect R package to detect complex spillover effects in real-world data.

Contribution

It develops a novel, design-based approach for causal inference in spatial experiments with interference, implemented through the SpatialEffect package.

Findings

Revealed substantial spatial spillovers in a conservation experiment

Demonstrated the effectiveness of the SpatialEffect package for causal analysis

Provided a step-by-step tutorial for practical application

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.