In this paper, we consider a setting where heterogeneous agents with connectivity are performing inference using unlabeled streaming data. Observed data are only partially informative about the target variable of interest. In order to overcome the uncertainty, agents cooperate with each other by exchanging their local inferences with and through a fusion center. To evaluate how each agent influences the overall decision, we adopt a causal framework in order to distinguish the actual influence of agents from mere correlations within the decision-making process. Various scenarios reflecting different agent participation patterns and fusion center policies are investigated. We derive expressions to quantify the causal impact of each agent on the joint decision, which could be beneficial for anticipating and addressing atypical scenarios, such as adversarial attacks or system malfunctions. We validate our theoretical results with numerical simulations and a real-world application of multi-camera crowd counting.

翻译：在本文中，我们考虑一个异构代理通过连接使用未标注流数据进行推理的场景。观测数据仅部分包含目标变量的信息。为了克服这种不确定性，代理通过融合中心交换各自的局部推理结果并进行协作。为评估每个代理对整体决策的影响，我们采用因果框架，以区分代理的实际影响与决策过程中单纯的关联关系。我们研究了反映不同代理参与模式和融合中心策略的各种场景。推导出量化每个代理对联合决策因果影响的表达式，这有助于预测和处理异常情况（如对抗攻击或系统故障）。通过数值模拟以及多摄像头人群计数的实际应用验证了理论结果。