Open Access
December 2017 Estimating average causal effects under general interference, with application to a social network experiment
Peter M. Aronow, Cyrus Samii
Ann. Appl. Stat. 11(4): 1912-1947 (December 2017). DOI: 10.1214/16-AOAS1005


This paper presents a randomization-based framework for estimating causal effects under interference between units motivated by challenges that arise in analyzing experiments on social networks. The framework integrates three components: (i) an experimental design that defines the probability distribution of treatment assignments, (ii) a mapping that relates experimental treatment assignments to exposures received by units in the experiment, and (iii) estimands that make use of the experiment to answer questions of substantive interest. We develop the case of estimating average unit-level causal effects from a randomized experiment with interference of arbitrary but known form. The resulting estimators are based on inverse probability weighting. We provide randomization-based variance estimators that account for the complex clustering that can occur when interference is present. We also establish consistency and asymptotic normality under local dependence assumptions. We discuss refinements including covariate-adjusted effect estimators and ratio estimation. We evaluate empirical performance in realistic settings with a naturalistic simulation using social network data from American schools. We then present results from a field experiment on the spread of anti-conflict norms and behavior among school students.


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Peter M. Aronow. Cyrus Samii. "Estimating average causal effects under general interference, with application to a social network experiment." Ann. Appl. Stat. 11 (4) 1912 - 1947, December 2017.


Received: 1 January 2016; Revised: 1 November 2016; Published: December 2017
First available in Project Euclid: 28 December 2017

zbMATH: 1383.62329
MathSciNet: MR3743283
Digital Object Identifier: 10.1214/16-AOAS1005

Keywords: Causal inference , interference , networks , potential outcomes , Randomization inference , SUTVA

Rights: Copyright © 2017 Institute of Mathematical Statistics

Vol.11 • No. 4 • December 2017
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