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Frameworks for causal inference

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Causal inference is treated here as the study of interventions. Various frameworks for distinguishing intervention from observation are discussed. Some frameworks assume the existence of latent deterministic mechanisms which marginalise to produce observed uncertainty. Other probabilistic frameworks are agnostic to such mechanisms. The semantics and computations in both types of frameworks are described and their relative advantages compared.

This talk is part of the Statistics Reading Group series.

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