  Reasoning and interacting with dynamic environments is a fundamental problem
in AI, but it becomes extremely challenging when actions can trigger cascades
of cross-dependent events. We introduce a new supervised learning setup called
{\em Cascade} where an agent is shown a video of a physically simulated dynamic
scene, and is asked to intervene and trigger a cascade of events, such that the
system reaches a "counterfactual" goal. For instance, the agent may be asked to
"Make the blue ball hit the red one, by pushing the green ball". The agent
intervention is drawn from a continuous space, and cascades of events makes the
dynamics highly non-linear.
  We combine semantic tree search with an event-driven forward model and devise
an algorithm that learns to search in semantic trees in continuous spaces. We
demonstrate that our approach learns to effectively follow instructions to
intervene in previously unseen complex scenes. It can also reason about
alternative outcomes, when provided an observed cascade of events.
