  Uncovering causal relationships in data is a major objective of data
analytics. Causal relationships are normally discovered with designed
experiments, e.g. randomised controlled trials, which, however are expensive or
infeasible to be conducted in many cases. Causal relationships can also be
found using some well designed observational studies, but they require domain
experts' knowledge and the process is normally time consuming. Hence there is a
need for scalable and automated methods for causal relationship exploration in
data. Classification methods are fast and they could be practical substitutes
for finding causal signals in data. However, classification methods are not
designed for causal discovery and a classification method may find false causal
signals and miss the true ones. In this paper, we develop a causal decision
tree where nodes have causal interpretations. Our method follows a well
established causal inference framework and makes use of a classic statistical
test. The method is practical for finding causal signals in large data sets.
