Causal inference isn't what you think it is
You may think that statistical causal inference is about inferring causation. You may think that it can not be tackled with standard statistical tools, but requires additional structure, such as counterfactual reasoning, potential responses or graphical representations. I shall try to disabuse you of such woolly misconceptions by locating statistical causality firmly within the scope of traditional statistical decision theory. From this viewpoint, the enterprise of "statistical causality" could fruitfully be rebranded as "assisted decision making".
Speaker
Professor Philip Dawid (University of Cambridge)
Philip Dawid FRS is Emeritus Professor of Statistics of the University of Cambridge. He has made fundamental contributions to both the philosophical underpinnings and the practical applications of Statistics. His theory of conditional independence is a keystone of modern statistical theory and methods, and he has demonstrated its usefulness in a host of applications, including computation in probabilistic expert systems, causal inference, and forensic identification. His co-authored book Probabilistic Networks and Expert Systems won the first DeGroot Prize for a published book in Statistical Science, and he was awarded the Royal Statistical Society's Guy Medal in Silver in 2001. He has served as Editor of Biometrika and of the Journal of the Royal Statistical Society (Series B), and as President of the International Society for Bayesian Analysis. He is an Emeritus Fellow of Darwin College Cambridge.
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