Causal inference has traditionally focused on interventions at the unit level. In many applications, however, the central question concerns the causal effects of connections between units, such as transportation links, social relationships, or collaborative ties. We develop a causal framework for edge interventions in networks, where treatments correspond to the presence or absence of edges. Our framework defines causal estimands under stochastic interventions on the network structure and introduces an inverse probability weighting estimator under an unconfoundedness assumption on edge assignment. We estimate edge probabilities using exponential random graph models, a widely used class of network models. We establish consistency and asymptotic normality of the proposed estimator. Finally, we apply our methodology to China's transportation network to estimate the causal impact of railroad connections on regional economic development.

翻译：传统因果推断主要关注单元层面的干预。然而，在许多应用中，核心问题涉及单元间连接（如交通线路、社会关系或协作纽带）的因果效应。本文针对网络中的边干预发展了一个因果推断框架，其中处理对应边的存在与否。该框架定义了网络结构随机干预下的因果估计量，并在边分配无混杂的假设下引入了一种逆概率加权估计器。我们使用指数随机图模型（一类广泛应用的网络模型）来估计边概率。我们证明了所提估计量的一致性与渐近正态性。最后，我们将该方法应用于中国交通网络，以估计铁路连接对区域经济发展的因果影响。