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Causation in Law and Statistics

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CIFW05 - Causal Machine Learning for the Social Sciences

My talk will examine the different approaches of law and statistics to the question of causation.  I will argue that while statistical method tends to see causation in terms which are ‘predictive’ and ‘generic’ (meaning that it identifies correlations between variables at the level of a population or sample, for purposes which include forecasting future effects), legal method tends to see causation as ‘postdictive’ and ‘individual’ (meaning that it is concerned with a retrospective allocation of responsibility for harm in an individual case).  These distinctions are not, however, watertight, and may be breaking down as more cases involving statistical evidence come before the courts.

This talk is part of the Isaac Newton Institute Seminar Series series.

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