A crucial aspect of understanding and reconstructing narratives is identifying the underlying causal chains, which explain why certain things happened and make a coherent story. To build such causal chains, we need to identify causal links between events in the story, which may be expressed explicitly as well as understood implicitly using commonsense knowledge.

This chapter reviews research efforts on the automated extraction of such event causality from natural language text. It starts with a brief review of existing causal models in psychology and psycholinguistics as a building block for understanding causation. These models are useful tools for guiding the annotation process to build corpora annotated with causal pairs. I then outline existing annotated resources, which are used to build and evaluate automated causality extraction systems. Furthermore, circumstantial events surrounding the causal complex are rarely expressed with language as they are part of common sense knowledge. Therefore, discovering causal common sense is also important to fill the gaps in the causal chains, and I discuss existing work in this line of research.