  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.
