Analyzing agent-based models is a complex task. Agent-based models typically contain
complex non-linear interactions between agents and generate emergent properties that
cannot easily be explained. They are most commonly analyzed using sensitivity analysis
techniques. While these techniques help understanding agent-based models better, they
are not a one-size-fits-all solution. This paper explores the novel use of causal discovery
algorithms from the field of causality as an additional means to analyze agent-based …