  The pharmaceutical industry is plagued by the problem of side effects that
can occur anytime a prescribed medication is ingested. There has been a recent
interest in using the vast quantities of medical data available in longitudinal
observational databases to identify causal relationships between drugs and
medical events. Unfortunately the majority of existing post marketing
surveillance algorithms measure how dependant or associated an event is on the
presence of a drug rather than measuring causality. In this paper we
investigate potential attributes that can be used in causal inference to
identify side effects based on the Bradford-Hill causality criteria. Potential
attributes are developed by considering five of the causality criteria and
feature selection is applied to identify the most suitable of these attributes
for detecting side effects. We found that attributes based on the specificity
criterion may improve side effect signalling algorithms but the experiment and
dosage criteria attributes investigated in this paper did not offer sufficient
additional information.
