Estimating total treatment effects in the presence of network interference typically requires knowledge of the underlying interaction structure. However, in many practical settings, network data is either unavailable, incomplete, or measured with substantial error. We demonstrate that causal message passing, a methodology that leverages temporal structure in outcome data rather than network topology, can recover total treatment effects comparable to network-aware approaches. We apply causal message passing to two large-scale field experiments where a recently developed bipartite graph methodology, which requires network knowledge, serves as a benchmark. Despite having no access to the interaction network, causal message passing produces effect estimates that match the network-aware approach in direction across all metrics and in statistical significance for the primary decision metric. Our findings validate the premise of causal message passing: that temporal variation in outcomes can serve as an effective substitute for network observation when estimating spillover effects. This has important practical implications: practitioners facing settings where network data is costly to collect, proprietary, or unreliable can instead exploit the temporal dynamics of their experimental data.

翻译：在网络干扰存在的情况下估计总处理效应通常需要了解底层的交互结构。然而，在许多实际场景中，网络数据要么不可用、不完整，要么存在显著测量误差。我们证明，因果消息传递这一方法利用结果数据中的时间结构而非网络拓扑，能够恢复与网络感知方法相当的总处理效应。我们将因果消息传递应用于两个大规模现场实验，其中一种最近开发的二分图方法（需要网络知识）作为基准。尽管无法访问交互网络，因果消息传递产生的效应估计在所有指标的方向上与网络感知方法一致，并在主要决策指标的统计显著性上与之匹配。我们的发现验证了因果消息传递的前提：在估计溢出效应时，结果的时间变化可以有效地替代网络观测。这具有重要的实际意义：面对网络数据收集成本高、专有或不可靠的场景，实践者可以转而利用其实验数据的时间动态。