  Understanding causality is key to the success of NLP applications, especially
in high-stakes domains. Causality comes in various perspectives such as enable
and prevent that, despite their importance, have been largely ignored in the
literature. This paper introduces a novel fine-grained causal reasoning dataset
and presents a series of novel predictive tasks in NLP, such as causality
detection, event causality extraction, and Causal QA. Our dataset contains
human annotations of 25K cause-effect event pairs and 24K question-answering
pairs within multi-sentence samples, where each can have multiple causal
relationships. Through extensive experiments and analysis, we show that the
complex relations in our dataset bring unique challenges to state-of-the-art
methods across all three tasks and highlight potential research opportunities,
especially in developing "causal-thinking" methods.
