This paper develops a sensitivity analysis framework that transfers the average total treatment effect (ATTE) from source data with a fully observed network to target data whose network is completely unknown. The ATTE represents the average social impact of a policy that assigns the treatment to every individual in the dataset. We postulate a covariate-shift type assumption that both source and target datasets share the same conditional mean outcome. However, because the target network is unobserved, this assumption alone is not sufficient to pin down the ATTE for the target data. To address this issue, we consider a sensitivity analysis based on the uncertainty of the target network's degree distribution, where the extent of uncertainty is measured by the Wasserstein distance from a given reference degree distribution. We then construct bounds on the target ATTE using a linear programming-based estimator. The limiting distribution of the bound estimator is derived via the functional delta method, and we develop a wild bootstrap approach to approximate the distribution. As an empirical illustration, we revisit the social network experiment on farmers' weather insurance adoption in China by Cai et al. (2015).

翻译：本文开发了一种敏感性分析框架，将平均总处理效应（ATTE）从具有完全观测网络的源数据迁移到网络完全未知的目标数据。ATTE代表了一项将处理分配给数据集中每个个体的政策的平均社会影响。我们假设一个协变量偏移类型的条件，即源数据集和目标数据集共享相同的条件均值结果。然而，由于目标网络未被观测到，仅凭这一假设不足以确定目标数据的ATTE。为解决此问题，我们考虑一种基于目标网络度分布不确定性的敏感性分析，其不确定性程度通过给定参考度分布的Wasserstein距离来度量。随后，我们利用一种基于线性规划的估计量构建目标ATTE的边界。通过泛函delta方法推导了边界估计量的极限分布，并开发了一种wild bootstrap方法来近似该分布。作为实证示例，我们重新审视了Cai等人（2015）关于中国农民天气保险采纳的社会网络实验。