The ability to perform causal and counterfactual reasoning are central\nproperties of human intelligence. Decision-making systems that can perform\nthese types of reasoning have the potential to be more generalizable and\ninterpretable. Simulations have helped advance the state-of-the-art in this\ndomain, by providing the ability to systematically vary parameters (e.g.,\nconfounders) and generate examples of the outcomes in the case of\ncounterfactual scenarios. However, simulating complex temporal causal events in\nmulti-agent scenarios, such as those that exist in driving and vehicle\nnavigation, is challenging. To help address this, we present a high-fidelity\nsimulation environment that is designed for developing algorithms for causal\ndiscovery and counterfactual reasoning in the safety-critical context. A core\ncomponent of our work is to introduce \\textit{agency}, such that it is simple\nto define and create complex scenarios using high-level definitions. The\nvehicles then operate with agency to complete these objectives, meaning\nlow-level behaviors need only be controlled if necessary. We perform\nexperiments with three state-of-the-art methods to create baselines and\nhighlight the affordances of this environment. Finally, we highlight challenges\nand opportunities for future work.\n