Frameworks for causal inference
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If you have a question about this talk, please contact Richard Samworth.
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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