  This paper investigates causal influences between agents linked by a social
graph and interacting over time. In particular, the work examines the dynamics
of social learning models and distributed decision-making protocols, and
derives expressions that reveal the causal relations between pairs of agents
and explain the flow of influence over the network. The results turn out to be
dependent on the graph topology and the level of information that each agent
has about the inference problem they are trying to solve. Using these
conclusions, the paper proposes an algorithm to rank the overall influence
between agents to discover highly influential agents. It also provides a method
to learn the necessary model parameters from raw observational data. The
results and the proposed algorithm are illustrated by considering both
synthetic data and real Twitter data.
