Some of the main users of statistical methods - economists, social scientists and epidemiologists - are discovering that their fields rest not on statistical but on causal foundations. These foundations have been blurred or avoided through the years for the lack of a mathematical notation capable of distinguishing causal from equational relationships. Recent advances in graphical methods provide formal and natural explication of these distinctions, and are destined to have a major impact on the way statistics is used in knowledge-rich applications. Statisticians, in response, are beginning to realize that causality is not a metaphysical dead-end, but a meaningful concept with clear mathematical underpinning. The paper surveys these transitions and outlines future challenges. (A)
Abstract