| Citation | Philip Dawid has made fundamental contributions both to the foundations of statistical inference, and to the development and dissemination of methods for handling complex uncertain evidence. In particular, he established the axiomatic basis for conditional independence and has promoted its use in graphical modelling, artificial intelligence, forensic science, and legal reasoning. Dawid's novel computational algorithms are widely used in Bayesian network software, while his general framework for reasoning about forensic identification evidence has now become standard. He is a leading international figure in the application of statistical science to the law. His foundational work includes a general predictive statistical methodology that connects Bayesian inference, stochastic complexity, algorithmic complexity and computational learning theory, while his decision-theoretic approach to causality brings insights and clarity to a wide range of causal problems. |