Jon Williamson, Bayesian Nets and Causality: Philosophical and Computational Foundations

Abstract

Jon Williamson is currently located at the Department of Philosophy, Logic, and Scientific Method, London School of Economics, London. Within this volume, Williamson seeks to address the age-old question of how we reason about causal relationships. From a philosophical perspective, this book explores the ontology and epistemology of probability and causality, whereas from a computational point of view, it investigates the relationship between Bayesian nets and maximum entropy methods. Williamson’s positions argued within this book, including objective Bayesianism and epistemic causality, are part of a coherent scientific framework in which entities are neither physical, mind-independent features of the world, nor arbitrary and subjective entities varying according to the individual. The aim of this book is to present coherent foundations to recent work hypothesizing that Bayesian nets provide a calculus for causal reasoning, and that one can learn actual causal relationships by the automated learning of Bayesian nets from observational data (i.e. inductively). The predictive features of Bayesian Systems’ products are based on a fundamental principal of logic known as Bayes’ theorem. This principle was discovered in 1761 by the Englishman Thomas Bayes, and brought into its modern form shortly thereafter by the great French mathematician Pierre Simon de Laplace. Properly understood, the theorem is the fundamental mathematical law governing the process of logical inference determining what degree of confidence we may have in various possible conclusions, based on the body of evidence available. Chapter one gives a coherent and concise overview of the entirety of the book. Chapter two gives one a foundational understanding of probability and its interpretations. Chapter three introduces the reader to Bayesian nets, and thereafter gives an erudite summary of them as well. Chapter four relates the problems that are accompanied by the attempt to apply Bayesian nets to causal reasoning, which leads