Estimating treatment effects in networks is challenging, as each potential outcome depends on the treatments of all other nodes in the network. To overcome this difficulty, existing methods typically impose an exposure mapping that compresses the treatment assignments in the network into a low-dimensional summary. However, if this mapping is misspecified, standard estimators for direct and spillover effects can be severely biased. We propose a novel partial identification framework for causal inference on networks to assess the robustness of treatment effects under misspecifications of the exposure mapping. Specifically, we derive sharp upper and lower bounds on direct and spillover effects under such misspecifications. As such, our framework presents a novel application of causal sensitivity analysis to exposure mappings. We instantiate our framework for three canonical exposure settings widely used in practice: (i) weighted means of the neighborhood treatments, (ii) threshold-based exposure mappings, and (iii) truncated neighborhood interference in the presence of higher-order spillovers. Furthermore, we develop orthogonal estimators for these bounds and prove that the resulting bound estimates are valid, sharp, and efficient. Our experiments show the bounds remain informative and provide reliable conclusions under misspecification of exposure mappings.

翻译：在网络中估计处理效应具有挑战性，因为每个潜在结果都依赖于网络中所有其他节点的处理状态。为克服这一困难，现有方法通常施加一个暴露映射，将网络中的处理分配压缩为一个低维摘要。然而，若该映射设定错误，针对直接效应和溢出效应的标准估计量可能产生严重偏差。我们提出了一种新颖的网络因果推断部分识别框架，用于评估暴露映射误设下处理效应的稳健性。具体而言，我们推导了在此类误设下直接效应和溢出效应的尖锐上界与下界。因此，本框架将因果敏感性分析创新性地应用于暴露映射。我们针对实践中广泛使用的三种典型暴露设定实例化了该框架：(i) 邻域处理状态的加权均值，(ii) 基于阈值的暴露映射，以及 (iii) 存在高阶溢出时的截断邻域干扰。此外，我们为这些界开发了正交估计量，并证明所得边界估计是有效、尖锐且高效的。实验表明，在暴露映射误设情况下，这些界仍能提供信息并得出可靠结论。