University of Cambridge > Talks.cam > Statistics > Selection bias, missing data and causal inference

  Browse A-Z

Log in

Googledetails Microsoftdetails

🔐 Log In

Information on

Subscribing to talksdetails Finding a talkdetails Adding a talkdetails Disseminating talksdetails Help and Documentationdetails

ℹī¸ Information

Selection bias, missing data and causal inference

Download to your calendar using vCal

  • UserKate Tilling, University of Bristol
  • ClockFriday 28 February 2020, 14:00-15:00
  • HouseMR12.

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:

Note that ex-directory lists are not shown.

Other lists

Room S2, Alison Richard Building, Sidgwick Site, 7 West Road, CB3 9DT Rm 11, Lecture Block, Sidgwick Site Perse Upper School, Hills Road, Cambridge

Other talks

Seminars in Cancer Dr Dinis Calado, Senior Group Leader, Immunity and Cancer Laboratory, the Francis Crick Institute Group Work Scattering Plenty: the people who made the modern countryside Title to be confirmed What Can a Spectrum Learn? AI Perspectives on the Kohn Laplacian of Sphere Quotients
 

Š 2006-2025 Talks.cam, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity