Heterogeneous Treatment and Spillover Effects Under Clustered Network Interference

63 Pages Posted: 15 Sep 2020 Last revised: 17 Sep 2020

See all articles by Falco Bargagli Stoffi

Falco Bargagli Stoffi

Harvard University

Costanza Tortù

IMT School for Advanced Studies Lucca, AXES Lab

Laura Forastiere

Yale University - Yale Institute for Network Science

Date Written: August 3, 2020

Abstract

The bulk of causal inference studies rules out the presence of interference between units. However, in many real-world settings units are interconnected by social, physical or virtual ties and the effect of a treatment can spill from one unit to other connected individuals in the network. In these settings, interference should be taken into account to avoid biased estimates of the treatment effect, but it can also be leveraged to save resources and provide the intervention to a lower percentage of the population where the treatment is more effective and where the effect can spill over to other susceptible individuals. In fact, different people might respond differently not only to the treatment received but also to the treatment received by their network contacts. Understanding the heterogeneity of treatment and spillover effects can help policy-makers in the scale-up phase of the intervention, it can guide the design of targeting strategies with the ultimate goal of making the interventions more cost-effective, and it might even allow generalizing the level of treatment spillover effects in other populations. In this paper, we develop a machine learning method that makes use of tree-based algorithms and an Horvitz-Thompson estimator to assess the heterogeneity of treatment and spillover effects with respect to individual, neighborhood and network characteristics in the context of clustered network interference. We illustrate how the proposed binary tree methodology performs in a Monte Carlo simulation study. Additionally, we provide an application on a randomized experiment aimed at assessing the heterogeneous effects of information sessions on the uptake of a new weather insurance policy in rural China.

Keywords: causal inference; potential outcomes; interference; social networks; machine learning; heterogeneous effects

JEL Classification: C87, C51, C52, C54, C55, C40, C13, D61, D62

Suggested Citation

Bargagli Stoffi, Falco and Tortù, Costanza and Forastiere, Laura, Heterogeneous Treatment and Spillover Effects Under Clustered Network Interference (August 3, 2020). Available at SSRN: https://ssrn.com/abstract=3666101 or http://dx.doi.org/10.2139/ssrn.3666101

Costanza Tortù

IMT School for Advanced Studies Lucca, AXES Lab ( email )

Piazza S. Francesco 19
Lucca, IT-55100
Italy

Laura Forastiere

Yale University - Yale Institute for Network Science ( email )

United States

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