  Using causal relations to guide decision making has become an essential
analytical task across various domains, from marketing and medicine to
education and social science. While powerful statistical models have been
developed for inferring causal relations from data, domain practitioners still
lack effective visual interface for interpreting the causal relations and
applying them in their decision-making process. Through interview studies with
domain experts, we characterize their current decision-making workflows,
challenges, and needs. Through an iterative design process, we developed a
visualization tool that allows analysts to explore, validate, and apply causal
relations in real-world decision-making scenarios. The tool provides an
uncertainty-aware causal graph visualization for presenting a large set of
causal relations inferred from high-dimensional data. On top of the causal
graph, it supports a set of intuitive user controls for performing what-if
analyses and making action plans. We report on two case studies in marketing
and student advising to demonstrate that users can effectively explore causal
relations and design action plans for reaching their goals.
