  Randomised controlled trials (RCTs) are the most effective approach to causal
discovery, but in many circumstances it is impossible to conduct RCTs.
Therefore observational studies based on passively observed data are widely
accepted as an alternative to RCTs. However, in observational studies, prior
knowledge is required to generate the hypotheses about the cause-effect
relationships to be tested, hence they can only be applied to problems with
available domain knowledge and a handful of variables. In practice, many data
sets are of high dimensionality, which leaves observational studies out of the
opportunities for causal discovery from such a wealth of data sources. In
another direction, many efficient data mining methods have been developed to
identify associations among variables in large data sets. The problem is,
causal relationships imply associations, but the reverse is not always true.
However we can see the synergy between the two paradigms here. Specifically,
association rule mining can be used to deal with the high-dimensionality
problem while observational studies can be utilised to eliminate non-causal
associations. In this paper we propose the concept of causal rules (CRs) and
develop an algorithm for mining CRs in large data sets. We use the idea of
retrospective cohort studies to detect CRs based on the results of association
rule mining. Experiments with both synthetic and real world data sets have
demonstrated the effectiveness and efficiency of CR mining. In comparison with
the commonly used causal discovery methods, the proposed approach in general is
faster and has better or competitive performance in finding correct or sensible
causes. It is also capable of finding a cause consisting of multiple variables,
a feature that other causal discovery methods do not possess.
