  Understanding multivariate extreme events play a crucial role in managing the
risks of complex systems since extremes are governed by their own mechanisms.
Conditional on a given variable exceeding a high threshold (e.g.\ traffic
intensity), knowing which high-impact quantities (e.g\ air pollutant levels)
are the most likely to be extreme in the future is key. This article
investigates the contribution of marginal extreme events on future extreme
events of related quantities. We propose an Extreme Event Propagation framework
to maximise counterfactual causation probabilities between a known cause and
future high-impact quantities. Extreme value theory provides a tool for
modelling upper tails whilst vine copulas are a flexible device for capturing a
large variety of joint extremal behaviours. We optimise for the probabilities
of causation and apply our framework to a London road traffic and air
pollutants dataset. We replicate documented atmospheric mechanisms beyond
linear relationships. This provides a new tool for quantifying the propagation
of extremes in a large variety of applications.
