# Design-Based Inference for Spatial Experiments with Interference

@article{Aronow2020DesignBasedIF, title={Design-Based Inference for Spatial Experiments with Interference}, author={Peter M. Aronow and Cyrus Samii and Ye Wang}, journal={arXiv: Methodology}, year={2020} }

We consider design-based causal inference in settings where randomized treatments have effects that bleed out into space in complex ways that overlap and in violation of the standard "no interference" assumption for many causal inference methods. We define a spatial "average marginalized response," which characterizes how, in expectation, units of observation that are a specified distance from an intervention point are affected by treatments at that point, averaging over effects emanating from… Expand

#### 12 Citations

Causal Inference for Spatial Treatments

- Computer Science, Economics
- 2020

A framework, estimators, and inference procedures for the analysis of causal effects in a setting with spatial treatments are proposed and a comparison between individuals near realized treatment locations and individuals near unrealized candidate locations is motivated. Expand

A graph‐theoretic approach to randomization tests of causal effects under general interference

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- Journal of the Royal Statistical Society: Series B (Statistical Methodology)
- 2021

Interference exists when a unit's outcome depends on another unit's treatment assignment. For example, intensive policing on one street could have a spillover effect on neighboring streets. Classical… Expand

Average Treatment Effects in the Presence of Interference

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We propose a definition for the average indirect effect of a binary treatment in the potential outcomes model for causal inference. Our definition is analogous to the standard definition of the… Expand

Causal Effects with Hidden Treatment Diffusion on Observed or Partially Observed Networks

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In randomized experiments, interactions between units might generate a treatment diffusion process. This is common when the treatment of interest is an actual object or product that can be shared… Expand

Causal Inference under Temporal and Spatial Interference

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- 2021

Many social events and policies generate spillover effects in both time and space. Their occurrence influences not only the outcomes of interest in the future, but also these outcomes in nearby… Expand

Children on the move: Progressive redistribution of humanitarian cash transfers among refugees

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- Journal of Development Economics
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Abstract We evaluate the impact of the Emergency Social Safety Net (ESSN) in Turkey, the largest cash transfer program for international refugees in the world. We provide prima facie evidence that… Expand

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- Economics
- 2021

This paper formalizes a common approach for estimating effects of treatment at a specific location using geocoded microdata. This estimator compares units immediately next to treatment (an… Expand

Inference in Spatial Experiments with Interference using the SpatialEffect Package

- Mathematics
- 2021

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… Expand

Rate-Optimal Cluster-Randomized Designs for Spatial Interference

- Mathematics, Economics
- 2021

We consider a potential outcomes model in which interference may be present between any two units but the extent of interference diminishes with spatial distance. The causal estimand is the global… Expand

Satellite-based deforestation alerts with training and incentives for patrolling facilitate community monitoring in the Peruvian Amazon

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- Proceedings of the National Academy of Sciences
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The results suggest that externally facilitated community-based monitoring protocols that combine remote-sensed early deforestation alerts with training and incentives for monitors could contribute to sustainable forest management. Expand

#### References

SHOWING 1-10 OF 22 REFERENCES

Bipartite Causal Inference with Interference.

- Medicine, Mathematics
- Statistical science : a review journal of the Institute of Mathematical Statistics
- 2021

This work introduces the setting of bipartite causal inference with interference, which arises when treatments are defined on observational units that are distinct from those at which outcomes are measured and there is interference between units in the sense that outcomes for some units depend on the treatments assigned to many other units. Expand

AVERAGE TREATMENT EFFECTS IN THE PRESENCE OF UNKNOWN INTERFERENCE.

- Medicine, Mathematics
- Annals of statistics
- 2021

The results imply that if one erroneously assumes that units do not interfere in a setting with limited, or even moderate, interference, standard estimators are nevertheless likely to be close to an average treatment effect if the sample is sufficiently large. Expand

Estimating average causal effects under general interference, with application to a social network experiment

- Mathematics, Computer Science
- 2017

This paper develops the case of estimating average unit-level causal effects from a randomized experiment with interference of arbitrary but known form and provides randomization-based variance estimators that account for the complex clustering that can occur when interference is present. Expand

Estimating Average Causal Effects Under General Interference

- Mathematics
- 2012

This paper presents randomization-based methods for estimating average causal effects under arbitrary interference of known form. Conservative estimators of the randomization variance of the average… Expand

Toward Causal Inference With Interference

- Psychology, Medicine
- Journal of the American Statistical Association
- 2008

This article considers a population of groups of individuals where interference is possible between individuals within the same group, and proposes estimands for direct, indirect, total, and overall causal effects of treatment strategies in this setting. Expand

On causal inference in the presence of interference

- Computer Science, Medicine
- Statistical methods in medical research
- 2012

This article summarises some of the concepts and results from the existing literature and extends that literature in considering new results for finite sample inference, new inverse probability weighting estimators in the presence of interference and new causal estimands of interest. Expand

Interference Between Units in Randomized Experiments

- Mathematics
- 2007

In a randomized experiment comparing two treatments, there is interference between units if applying the treatment to one unit may affect other units. Interference implies that treatment effects are… Expand

Reasoning about Interference Between Units: A General Framework

- Computer Science
- Political Analysis
- 2013

This article shows how counterfactual causal models may be written and tested when theories suggest spillover or other network-based interference among experimental units, and offers researchers the ability to model theories about how treatment given to some units may come to influence outcomes for other units. Expand

Causal inference for social network data

- Mathematics
- 2017

We extend recent work by van der Laan (2014) on causal inference for causally connected units to more general social network settings. Our asymptotic results allow for dependence of each observation… Expand

How Much Should We Trust Estimates from Multiplicative Interaction Models? Simple Tools to Improve Empirical Practice

- Computer Science, Mathematics
- 2016

Replicating nearly 50 interaction effects from 22 recent publications in five top political science journals finds that these core assumptions fail in a majority of cases, suggesting that a large portion of findings across all subfields based on interaction models are modeling artifacts or are at best highly model dependent. Expand