Selection bias, missing data and causal inference

Selection bias, missing data and causal inference

Causal inference can be attempted using different statistical methods, each of which require some (untestable) assumptions. Common methods include multivariable regression (no unmeasured confounding), variations on regression such as propensity scores, g-methods (no unmeasured confounding) and instrumental variables (no association between instrument and outcome, other than via the exposure).

Less attention has been given to the impact of selection (e.g. selection into a study, analysis of cases only) or missing data (e.g. dropout from a study, death due to other causes) on causal inference. Using directed acyclic graphs (DAGs) Professor Kate Tilling will show some of the ways in which bias can occur due to selection or missing data.

Applied work shows evidence of non-random selection into and dropout from studies including ALSPAC and UK Biobank, and Professor Tilling will discuss how this might impact causal analyses using these datasets.

Speaker

Professor Kate Tilling, University of Bristol.

How to attend

If not a member of the Dept. Mathematical Science at the University of Essex, you can register your interest in attending the seminar and request the Zoom’s meeting password by emailing Dr Osama Mahmoud.