Traditionally, statistical inference and causal inference on human subjects rely on the assumption that individuals are independently affected by treatments or exposures. However, recently there has been increasing interest in settings, such as social networks, where individuals may interact with one another such that treatments may spill over from the treated individual to their social contacts and outcomes may be contagious. Existing models proposed for causal inference using observational data from networks of interacting individuals have two major shortcomings. First, they often require a level of granularity in the data that is infeasible in practice to collect in most settings and, second, the models are high dimensional and often too big to fit to the available data. We illustrate and justify a parsimonious parameterization for network data with interference and contagion. Our parameterization corresponds to a particular family of graphical models known as chain graphs. We argue that, in some settings, chain graph models approximate the marginal distribution of a snapshot of a longitudinal data‐generating process on interacting units. We illustrate the use of chain graphs for causal inference about collective decision making in social networks by using data from US Supreme Court decisions between 1994 and 2004 and in simulations.

中文翻译：

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因果推理、社交网络和链图


传统上，对人类受试者的统计推断和因果推断依赖于个体独立地受到治疗或暴露影响的假设。然而，最近人们对诸如社交网络之类的环境越来越感兴趣，在社交网络中，个体可以彼此互动，这样治疗可能会从接受治疗的个体蔓延到他们的社交接触中，并且结果可能会传染。使用来自交互个体网络的观察数据进行因果推断的现有模型有两个主要缺点。首先，它们通常需要一定程度的数据粒度，而这在大多数情况下在实践中收集是不可行的；其次，模型是高维的，并且通常太大而无法适应可用数据。我们说明并证明了具有干扰和传染的网络数据的简约参数化。我们的参数化对应于称为链图的特定图形模型系列。我们认为，在某些情况下，链图模型近似于交互单元上纵向数据生成过程快照的边际分布。我们通过使用美国最高法院 1994 年至 2004 年间判决的数据和模拟来说明如何使用链图对社交网络中的集体决策进行因果推断。