Inference in Spatial Experiments with Interference using the SpatialEffect Package

06/29/2021 ∙ by Peter M. Aronow, et al. ∙ 0

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 point that is a given distance away, averaging ambient effects emanating from other intervention points. 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.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 10

page 11

page 13

page 20

page 21

This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.