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Graphical models for causal reasoning in epidemiology

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Graphical models are used to represent conditional independencies in multivariate systems; they can facilitate model formulation, reasoning and communication with subject matter experts as well as computations. Here I will consider in particular their use for reasoning about causal inference in epidemiology, where we typically want to investigate the effect of an intervention, e.g. a public health intervention like banning smoking in pubs and restaurants, on some health outcome. This task is challenging because most epidemiological studies are based on observational data and not on randomised studies. We therefore have to deal with the problem that an observed association between exposure and outcome can be due to many other phenomena apart from an actual causal relation, e.g. confounding, reverse causation, selection

This talk is part of the MRC Biostatistics Unit Seminars series.

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