Using a bigram event model to predict causal relations

This paper addresses the problem of causal knowledge discovery. Using online screenplays, we generate a corpus of temporally ordered events. We then introduce a measure we call causal potential which is easily calculated with statistics gathered over the corpus and show that this measure is highly correlated with an event pair's tendency of encoding a causal relation. We suggest that causal potential can be used in systems whose task is to determine the existence of causality between temporally adjacent events, when critical context is either missing or unreliable. Moreover, we argue that our model should therefore be used as a baseline for standard supervised models which take into account contextual information.