  Causality visualization can help people understand temporal chains of events,
such as messages sent in a distributed system, cause and effect in a historical
conflict, or the interplay between political actors over time. However, as the
scale and complexity of these event sequences grows, even these visualizations
can become overwhelming to use. In this paper, we propose the use of textual
narratives as a data-driven storytelling method to augment causality
visualization. We first propose a design space for how textual narratives can
be used to describe causal data. We then present results from a crowdsourced
user study where participants were asked to recover causality information from
two causality visualizations--causal graphs and Hasse diagrams--with and
without an associated textual narrative. Finally, we describe CAUSEWORKS, a
causality visualization system for understanding how specific interventions
influence a causal model. The system incorporates an automatic textual
narrative mechanism based on our design space. We validate CAUSEWORKS through
interviews with experts who used the system for understanding complex events.
