Identification and Estimation of Treatment and Interference Effects in Observational Studies on Networks
Identification and Estimation of Treatment and Interference Effects in Observational Studies on Networks
复制标题
网络观测研究中治疗和干扰效应的识别和估计
DOI：
10.1080/01621459.2020.1768100
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发表时间：
2020-06-26
影响因子：
3.7
通讯作者：
Mealli, Fabrizia
中科院分区：
文献类型：
--
作者：
Forastiere, Laura;Airoldi, Edoardo M.;Mealli, Fabrizia
对通过网络连接的一组单元进行因果推断通常会带来技术挑战，包括如何解释干扰。例如，在存在干扰的情况下，一个单元的潜在结果取决于它们的处理以及其他单元的处理，例如它们在网络中的邻居。在观察性研究中，更复杂的是，典型的无混淆假设必须扩展，比如，包括邻居的处理，以及个人和邻居的协变量，以保证识别和有效的推理。在这里，我们提出了新的估计，定义治疗和干扰效果。然后，我们推导出一个天真的估计，错误地假设远离干扰的偏差的解析表达式。偏差取决于干扰的程度，但也取决于个人和邻居之间的处理的关联程度。我们提出了一个扩展的unconfoundedness假设，占干扰，我们开发了新的协变量调整方法，导致有效的估计治疗和网络上的观察性研究中的干扰效应。估计是基于一个广义的倾向得分，平衡个人和邻里协变量在不同水平的个人治疗和暴露于邻居的治疗单位。我们进行了模拟，校准使用友谊网络和协变量在全国代表性的纵向研究，青少年在等级7-12在美国，探索有限样本的性能在不同的现实环境。
Causal inference on a population of units connected through a network often presents technical challenges, including how to account for interference. In the presence of interference, for instance, potential outcomes of a unit depend on their treatment as well as on the treatments of other units, such as their neighbors in the network. In observational studies, a further complication is that the typical unconfoundedness assumption must be extended-say, to include the treatment of neighbors, and individual and neighborhood covariates-to guarantee identification and valid inference. Here, we propose new estimands that define treatment and interference effects. We then derive analytical expressions for the bias of a naive estimator that wrongly assumes away interference. The bias depends on the level of interference but also on the degree of association between individual and neighborhood treatments. We propose an extended unconfoundedness assumption that accounts for interference, and we develop new covariate-adjustment methods that lead to valid estimates of treatment and interference effects in observational studies on networks. Estimation is based on a generalized propensity score that balances individual and neighborhood covariates across units under different levels of individual treatment and of exposure to neighbors' treatment. We carry out simulations, calibrated using friendship networks and covariates in a nationally representative longitudinal study of adolescents in grades 7-12 in the United States, to explore finite-sample performance in different realistic settings.for this article are available online.