AI algorithms struggle to recognise events or objects in contexts that are different from the training set. A situation in the world is something that has no boundaries at all, you don't know what's in the situation, what's out of the situation.") If you're trying to train an AI to deal with this "unframed" world, you run into a lot of challenges. Humans learn about causal relationships by making interventions/actions in a given environment, observing the result, then refining the mental model they've "built" by making similar actions in subtly different environments in the great, fluid thing that is The World. It's hard to build AI training sets that can help algorithms "understand" the myriad causal relationships taking place at any given time in a similar way; rather than train them to understand more fixed patterns of behaviour: e.g. the hard numbers that need to be crunched to beat a human in a game of tightly circumscribed mathematical probabilities like chess.

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.