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Causal Counterfactual Theory for the Attribution of Weather and Climate-Related Events

Abstract

Causal counterfactual theory provides clear semantics and sound logic for causal reasoning and may help foster research on, and clarify dissemination of, weather and climate-related event attribution. The current event attribution framework obeys the spirit of counterfactual logics. The author has proposed a methodology, which relies on in silico experimentation to derive both the factual and counterfactual probabilities. The attributable proportion are also used to name the same quantity. The FAR, as well as these similar terms, is used to communicate the idea, particularly relevant in epidemiology from which it originates, that the exposition to a given risk factor X translates into an increase of, say, the frequency of a given disease Y. In causal theory, the probability of necessary causation PN formally embeds the notion of causal attribution in its definition. Finally, at a more practical level, attribution studies applying causal theory require the availability of counterfactual model simulations.

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