  When does a sequence of events define an everyday scenario and how can this
knowledge be induced from text? Prior works in inducing such scripts have
relied on, in one form or another, measures of correlation between instances of
events in a corpus. We argue from both a conceptual and practical sense that a
purely correlation-based approach is insufficient, and instead propose an
approach to script induction based on the causal effect between events,
formally defined via interventions. Through both human and automatic
evaluations, we show that the output of our method based on causal effects
better matches the intuition of what a script represents
