Mining Combined Causes in Large Data Sets
  In recent years, many methods have been developed for detecting causal
relationships in observational data. Some of them have the potential to tackle
large data sets. However, these methods fail to discover a combined cause, i.e.
a multi-factor cause consisting of two or more component variables which
individually are not causes. A straightforward approach to uncovering a
combined cause is to include both individual and combined variables in the
causal discovery using existing methods, but this scheme is computationally
infeasible due to the huge number of combined variables. In this paper, we
propose a novel approach to address this practical causal discovery problem,
i.e. mining combined causes in large data sets. The experiments with both
synthetic and real world data sets show that the proposed method can obtain
high-quality causal discoveries with a high computational efficiency.
