Historically, causal markers, syntactic structures and connectives have been the sole identifying features for automatically extracting causal relations in natural language discourse. However various connectives such as "and", prepositions such as "as" and other syntactic structures are highly ambiguous in nature, and it is clear that one cannot solely rely on lexico-syntactic markers for detection of causal phenomenon in discourse. This paper introduces the theory of granularity and describes different approaches to identify granularity in natural language. As causality is often granular in nature (Mazlack 2004), we use granularity relations to discover and infer the presence of causal relations in text. We compare this with causal relations identified using just causal markers. We achieve a precision of 0.91 and a recall of 0.79 using granularity for causal relation detection, as compared to a precision of 0.79 and a recall of 0.44 using pure causal markers for causality detection.
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