论文标题
网络合成干预措施：网络干扰下面板数据的因果框架
Network Synthetic Interventions: A Causal Framework for Panel Data Under Network Interference
论文作者
论文摘要
我们提出了合成控制和合成干预方法的概括，以纳入网络干扰。我们考虑在跨单位溢出和未观察到的混杂的情况下，在面板数据中对单位特异性潜在结果的估计。我们方法的关键是一种新型的潜在因子模型，它考虑了网络干扰，并概括了面板数据设置中通常使用的因子模型。我们提出了一个估计器，网络合成干预措施（NSI），并表明它始终估算一个在网络的任意反事实治疗集中的单位的平均结果。我们进一步确定估计量在渐近上是正常的。我们为NSI估计值是否可靠地概括为产生准确的反事实估计值提供了两个有效性测试。我们提供了一种新型的基于图的实验设计，可确保NSI估计器产生准确的反事实估计，并分析所提出的设计的样品复杂性。我们以证实我们理论发现的模拟结论。
We propose a generalization of the synthetic controls and synthetic interventions methodology to incorporate network interference. We consider the estimation of unit-specific potential outcomes from panel data in the presence of spillover across units and unobserved confounding. Key to our approach is a novel latent factor model that takes into account network interference and generalizes the factor models typically used in panel data settings. We propose an estimator, Network Synthetic Interventions (NSI), and show that it consistently estimates the mean outcomes for a unit under an arbitrary set of counterfactual treatments for the network. We further establish that the estimator is asymptotically normal. We furnish two validity tests for whether the NSI estimator reliably generalizes to produce accurate counterfactual estimates. We provide a novel graph-based experiment design that guarantees the NSI estimator produces accurate counterfactual estimates, and also analyze the sample complexity of the proposed design. We conclude with simulations that corroborate our theoretical findings.
