ESTIMATING AVERAGE CAUSAL EFFECTS UNDER GENERAL INTERFERENCE, WITH APPLICATION TO A SOCIAL NETWORK EXPERIMENT
ESTIMATING AVERAGE CAUSAL EFFECTS UNDER GENERAL INTERFERENCE, WITH APPLICATION TO A SOCIAL NETWORK EXPERIMENT
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DOI：
10.1214/16-aoas1005
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发表时间：
2017-12-01
影响因子：
1.8
通讯作者：
Samii, Cyrus
中科院分区：
文献类型：
--
作者：
Aronow, Peter M.;Samii, Cyrus
本文提出了一个基于随机化的框架，用于估计在社会网络分析实验中出现的挑战驱动的单元之间干扰下的因果效应。该框架集成了三个组成部分：(i)定义处理分配概率分布的实验设计，（ii）将实验处理分配与实验中单位所接受的暴露联系起来的映射，以及（iii）利用实验来回答实质性问题的估计。我们发展的情况下，估计平均单位水平的因果效应，从随机实验的干扰任意但已知的形式。所得估计量基于逆概率加权。我们提供了基于随机化的方差估计器，用于解释存在干扰时可能发生的复杂聚类。在局部相关的假设下，我们也建立了一致性和渐近正态性。我们讨论了包括协变量调整效应估计和比率估计在内的改进方法。我们使用来自美国学校的社交网络数据，通过自然模拟来评估现实环境中的经验表现。然后，我们展示了一项关于在校学生中反冲突规范和行为传播的实地实验结果。
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.