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Randomised controlled trials (RCTs) are the most effective approach to causaldiscovery, but in many circumstances it is impossible to conduct RCTs.Therefore observational studies based on passively observed data are widelyaccepted as an alternative to RCTs. However, in observational studies, priorknowledge is required to generate the hypotheses about the cause-effectrelationships to be tested, hence they can only be applied to problems withavailable domain knowledge and a handful of variables. In practice, many datasets are of high dimensionality, which leaves observational studies out of theopportunities for causal discovery from such a wealth of data sources. Inanother direction, many efficient data mining methods have been developed toidentify 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-dimensionalityproblem while observational studies can be utilised to eliminate non-causalassociations. In this paper we propose the concept of causal rules (CRs) anddevelop an algorithm for mining CRs in large data sets. We use the idea ofretrospective cohort studies to detect CRs based on the results of associationrule mining. Experiments with both synthetic and real world data sets havedemonstrated the effectiveness and efficiency of CR mining. In comparison withthe commonly used causal discovery methods, the proposed approach in general isfaster and has better or competitive performance in finding correct or sensiblecauses. It is also capable of finding a cause consisting of multiple variables,a feature that other causal discovery methods do not possess.

From Observational Studies to Causal Rule Mining