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29 January 2019

This talk is about causal inference and estimands from the perspective of DAGs (directed acyclic graphs), which are widely used for causal inference in observational studies. DAGs allow complex statistical issues to be represented pictorially yet still rigorously. Ian briefly describes the ideas of DAGs, then draws suitable DAGs for randomised trials with non-randomised treatment changes and uses them to discuss some key estimands and how these may be estimated. The talk is conceptual rather than mathematical, and points to types of approach rather than specific approaches.

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