One core assumption typically adopted for valid causal inference is that of no interference between experimental units, i.e., the outcome of an experimental unit is unaffected by the treatments assigned to other experimental units. This assumption can be violated in real-life experiments, which significantly complicates the task of causal inference as one must disentangle direct treatment effects from ``spillover'' effects. Current methodologies are lacking, as they cannot handle arbitrary, unknown interference structures to permit inference on causal estimands. We present a general framework to address the limitations of existing approaches. Our framework is based on the new concept of the ``degree of interference'' (DoI). The DoI is a unit-level latent variable that captures the latent structure of interference. We also develop a data augmentation algorithm that adopts a blocked Gibbs sampler and Bayesian nonparametric methodology to perform inferences on the estimands under our framework. We illustrate the DoI concept and properties of our Bayesian methodology via extensive simulation studies and an analysis of a randomized experiment investigating the impact of a cash transfer program for which interference is a critical concern. Ultimately, our framework enables us to infer causal effects without strong structural assumptions on interference.

翻译：因果推断通常采用的核心假设之一是实验单元之间无干扰，即某个实验单元的结果不受其他实验单元所分配处理的影响。这一假设在实际实验中可能被违反，从而显著复杂化因果推断任务，因为必须从“溢出”效应中剥离出直接处理效应。现有方法存在不足，无法处理任意未知的干扰结构以支持因果估计量的推断。我们提出了一个通用框架来解决现有方法的局限性。该框架基于新概念“干扰程度”（DoI）。DoI是一个单元级潜在变量，用于捕获干扰的潜在结构。我们还开发了一种数据增广算法，采用分块吉布斯采样器和贝叶斯非参数方法，在该框架下进行估计量的推断。通过广泛的模拟研究以及一项针对现金转移项目影响的随机实验分析（该实验中干扰是关键关注点），我们展示了DoI概念及贝叶斯方法论的属性和效果。最终，我们的框架无需对干扰做出强结构假设即可推断因果效应。