Selection bias, missing data and causal inference

Add to your list(s) Download to your calendar using vCal

• Kate Tilling, University of Bristol
• Friday 28 February 2020, 14:00-15:00
• MR12.

If you have a question about this talk, please contact Dr Sergio Bacallado.

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) I will show some of the ways in which bias can occur due to selection or missing data. I will also present a recently developed method to overcome selection bias using genetic data (under some assumptions). Applied work shows evidence of non-random selection into and dropout from studies including ALSPAC and UK Biobank, and I will discuss how this might impact causal analyses using these datasets.

This talk is part of the Statistics series.

This talk is included in these lists:

• All CMS events
• All Talks (aka the CURE list)
• CMS Events
• Cambridge Forum of Science and Humanities
• Cambridge Language Sciences
• Cambridge talks
• Chris Davis' list
• DPMMS Lists
• DPMMS info aggregator
• DPMMS lists
• Guy Emerson's list
• Hanchen DaDaDash
• Interested Talks
• MR12
• Machine Learning
• School of Physical Sciences
• Statistical Laboratory info aggregator
• Statistics
• Statistics Group
• bld31
• custom
• rp587

Note that ex-directory lists are not shown.