Statistical aspects of causality are reviewed in simple form and the impact of recent work discussed. Three distinct notions of
causality are set out and implications for densities and for linear dependencies explained. The importance of appreciating
the possibility of effect modifiers is stressed, be they intermediate variables, background variables or unobserved confounders.
In many contexts the issue of unobserved confounders is salient. The difficulties of interpretation when there are joint effects
are discussed and possible modifications of analysis explained. The dangers of uncritical conditioning and marginalization over
intermediate response variables are set out and some of the problems of generalizing conclusions to populations and individuals
explained. In general terms the importance of search for possibly causal variables is stressed but the need for caution is emphasized.