  Being able to reason about how one's behaviour can affect the behaviour of
others is a core skill required of intelligent driving agents. Despite this,
the state of the art struggles to meet the need of agents to discover causal
links between themselves and others. Observational approaches struggle because
of the non-stationarity of causal links in dynamic environments, and the
sparsity of causal interactions while requiring the approaches to work in an
online fashion. Meanwhile interventional approaches are impractical as a
vehicle cannot experiment with its actions on a public road. To counter the
issue of non-stationarity we reformulate the problem in terms of extracted
events, while the previously mentioned restriction upon interventions can be
overcome with the use of counterfactual simulation. We present three variants
of the proposed counterfactual causal discovery method and evaluate these
against state of the art observational temporal causal discovery methods across
3396 causal scenes extracted from a real world driving dataset. We find that
the proposed method significantly outperforms the state of the art on the
proposed task quantitatively and can offer additional insights by comparing the
outcome of an alternate series of decisions in a way that observational and
interventional approaches cannot.
