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Causality and Decision Theory: A Statistical Perspective

In recent years Statisticians, Economists, Epidemiologists and others have paid increased attention to making and justifying causal inferences on the basis of data. They have used a variety of underlying philosophies - e.g. determinism, counterfactuals; of representational frameworks - e.g. algebraic, graphical; and of the methodological approaches - e.g. potential response models, do-calculus. But beneath this diversity there appears to be general agreement that causal inference necessitates some special machinery, over and above what has traditionally been adequate for statistical modelling. I believe that this is mistaken, and that the additional mathematical richness of such frameworks only promotes confusion, paradox and error - even at its best overcomplicating what should be straightforward. The vast majority of problems of practical causal inference can be simply and fruitfully understood, formulated and solved using already well-established probabilistic language and methodology, especially conditional independence and decision theory. They can also benefit from established decision aids such as influence diagrams. In this talk I shall argue against the richer frameworks, and in favour of a simple decision-theoretic approach. I shall indicate how this clarifies such issues as confounding in observational studies and sequential treatment regimes, and provides protection against over-glib causal interpretation of statistical data.

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.