Can we improve our understanding of cardiovascular disease (CVD) causality and prediction? Intuitively, we can. Recent publications, however, could be misinterpreted as suggesting the opposite. First, results of the Interheart study, which indicated that nine conventional risk factors explain 90% of premature myocardial infarction, could be interpreted as meaning that other 'new' cardiovascular risk factors could only cause a small fraction of disease, at most 10%. Secondly, papers addressing the predictive value of new risk factors or markers of early CVD risk have concluded that the addition of these variables to risk models does not improve risk prediction. In this article, we explain that searching for 'new causes' of CVD is still highly relevant, and that improvement of risk prediction is often assessed using inappropriate statistical methodology.