Most are familiar with the adage correlation does not imply causation. Since much of science is concerned with problems of causality and statistics is so widely used in research, one may wonder whether statistics possesses the tools to study such problems and contribute to their resolution. These were the questions posed over thirty years ago by Pearl, Robins, Rubin, Shafer, etc. when they set out to incorporate notions of causality into statistics theory and develop methods for estimating causal relationships. Since then, the schools of statistical causality they founded have produced interesting results and methods that help us think about causality and are potentially useful in real-life problems. Yet, despite its appeal, statistical causality is still disregarded by many mainstream statisticians, and its methods are not widely known. In part this is explained by the unorthodox and apparently disparate character of the various schools, in particular by the distinct languages they de