  The ability to perform causal and counterfactual reasoning are central
properties of human intelligence. Decision-making systems that can perform
these types of reasoning have the potential to be more generalizable and
interpretable. Simulations have helped advance the state-of-the-art in this
domain, by providing the ability to systematically vary parameters (e.g.,
confounders) and generate examples of the outcomes in the case of
counterfactual scenarios. However, simulating complex temporal causal events in
multi-agent scenarios, such as those that exist in driving and vehicle
navigation, is challenging. To help address this, we present a high-fidelity
simulation environment that is designed for developing algorithms for causal
discovery and counterfactual reasoning in the safety-critical context. A core
component of our work is to introduce \textit{agency}, such that it is simple
to define and create complex scenarios using high-level definitions. The
vehicles then operate with agency to complete these objectives, meaning
low-level behaviors need only be controlled if necessary. We perform
experiments with three state-of-the-art methods to create baselines and
highlight the affordances of this environment. Finally, we highlight challenges
and opportunities for future work.
