Randomized experiments are a powerful methodology for data-driven evaluation of decisions or interventions. Yet, their validity may be undermined by network interference. This occurs when the treatment of one unit impacts not only its outcome but also that of connected units, biasing traditional treatment effect estimations. Our study introduces a new framework to accommodate complex and unknown network interference, moving beyond specialized models in the existing literature. Our framework, which we term causal message-passing, is grounded in a high-dimensional approximate message passing methodology and is specifically tailored to experimental design settings with prevalent network interference. Utilizing causal message-passing, we present a practical algorithm for estimating the total treatment effect and demonstrate its efficacy in four numerical scenarios, each with its unique interference structure.

翻译：随机实验是数据驱动决策或干预评估的强大方法。然而，网络干扰可能削弱其有效性。当某个实验单元的处理不仅影响其自身结果，还影响其他相连单元的结果时，传统处理效应估计会产生偏差。本研究提出了一种新框架，以应对复杂且未知的网络干扰，超越了现有文献中的特定模型。该框架被称为因果消息传递，基于高维近似消息传递方法，并专门针对存在普遍网络干扰的实验设计场景而定制。利用因果消息传递，我们提出了一种估算总处理效应的实用算法，并在四种具有独特干扰结构的数值场景中展示了其有效性。