Statistics, Causality, and Graphs

Abstract

Some of the main users of statistical methods -economists, social scientists, and epidemiologists -are discovering that their fields rest not on statisti­ cal but on causal foundations. The blurring of these foundations over the years follows from the lack of mathematical notation capable of distinguish­ ing causal from equational relationships. By providing formal and natural explication of such relations, graphical methods have the potential to revolu­ tionize how 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 developments and outlines future challenges.