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With the surge in the availability of information, there is a great demand for tools that assist users in understanding their data. While today's exploration tools rely mostly on data visualization, users often want to go deeper and understand the underlying causes of a particular observation. This tutorial surveys research on causality and explanation for data-oriented applications. We will review and summarize the research thus far into causality and explanation in the database and AI communities, giving researchers a snapshot of the current state of the art on this topic, and propose a unified framework as well as directions for future research. We will cover both the theory of causality/explanation and some applications; we also discuss the connections with other topics in database research like provenance, deletion propagation, why-not queries, and OLAP techniques.
Meliou et al. (2014) studied this question.