  Agent-based simulators provide granular representations of complex
intelligent systems by directly modelling the interactions of the system's
constituent agents. Their high-fidelity nature enables hyper-local policy
evaluation and testing of what-if scenarios, but is associated with large
computational costs that inhibits their widespread use. Surrogate models can
address these computational limitations, but they must behave consistently with
the agent-based model under policy interventions of interest. In this paper, we
capitalise on recent developments on causal abstractions to develop a framework
for learning interventionally consistent surrogate models for agent-based
simulators. Our proposed approach facilitates rapid experimentation with policy
interventions in complex systems, while inducing surrogates to behave
consistently with high probability with respect to the agent-based simulator
across interventions of interest. We demonstrate with empirical studies that
observationally trained surrogates can misjudge the effect of interventions and
misguide policymakers towards suboptimal policies, while surrogates trained for
interventional consistency with our proposed method closely mimic the behaviour
of an agent-based model under interventions of interest.
