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CAUSALITY: CONCEPTIONS, CONNEXIONS, CONFUSIONS, CONTENTIONS

Modern statistical approaches to causal inference are based on a variety of distinct foundations, ingredients, assumptions and methods. These involve differing conceptions of the effects of interventions, or of stable relationships across regimes; disagreement over the roles of hypothetical and counterfactual outcomes; and varying semantics and uses for algebraic, graphical and other representations. There does however seem to be fairly broad agreement that causal inference requires significant modifications and extensions to standard statistical machinery. I shall argue that this is mistaken, and that the power of existing statistical and decision-theoretic tools to address causal issues is much greater than is commonly allowed.

Speaker

Philip Dawid is Professor of Statistics at Cambridge University, having been Pearson Professor of Statistics at University College London from 1989 to 2007. He is Chartered Statistician and Fellow of the Royal Statistical Society, which has awarded him Guy Medals in Bronze and Silver; elected Fellow of the Institute of Mathematical Statistics; elected Member of the International Statistical Institute; and a Member of the Organising Committee for the Valencia International Meetings on Bayesian Statistics. He has served as Editor of the Journal of the Royal Statistical Society (Series B) and of Biometrika, and is currently an Editor of Bayesian Analysis. He was President of the International Society for Bayesian Analysis for the year 2000.