  With the increasing need of personalised decision making, such as
personalised medicine and online recommendations, a growing attention has been
paid to the discovery of the context and heterogeneity of causal relationships.
Most existing methods, however, assume a known cause (e.g. a new drug) and
focus on identifying from data the contexts of heterogeneous effects of the
cause (e.g. patient groups with different responses to the new drug). There is
no approach to efficiently detecting directly from observational data context
specific causal relationships, i.e. discovering the causes and their contexts
simultaneously. In this paper, by taking the advantages of highly efficient
decision tree induction and the well established causal inference framework, we
propose the Tree based Context Causal rule discovery (TCC) method, for
efficient exploration of context specific causal relationships from data.
Experiments with both synthetic and real world data sets show that TCC can
effectively discover context specific causal rules from the data.
