Causal Impact Analysis for Asynchronous Decision Making

We consider a collaborative decision-making framework where heterogeneous agents receive streaming and partially informative observations. We consider two asynchronous scenarios that differ based on the agents' participation patterns and the fusion center's policies. By using hypothetical interventions on individual agents to conduct credit assignment, we attribute causal impact scores to each agent for the joint decision. By further employing these scores in a guided theoretical analysis, we compare the fusion center's two policies by evaluating their vulnerability to adversarial attacks, robustness against moderate deviations, and fairness.