Elements of causal inference: foundations and learning algorithms

摘要

Data-based resolution of uncertainty in science must deal with two largely orthogonal issues: doubt (the degree of belief that one has in a scientific proposition) and ambiguity (one's understanding of the proposition). (This critical distinction is articulated most clearly in.) Throughout the 20th century, most of the focus in statistics was on doubt, with emphasis on artifacts like correlation coefficients and p-values. In the last two decades of the century, statisticians such as Pearl, Spirtes, Glymour, and Schemes began to develop formal methods for addressing ambiguity (present later syntheses of their work, which began in the 1980s). Relying heavily on graphical models in which arrows from one state variable to another indicate causal influence, they showed how to analyze the causal structures that generate data, attacking the problem of ambiguity. The use of "elements" in the title is misleading, for this volume includes much recently published research and original results by the authors.