On causal inference and self-selection
Seminar
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Bio
Elena Stanghellini is Professor of Statistics at the University of Perugia. After completing her Ph.D. in 1995 at the University of Florence, she was Research Fellow at the Open University, to then join the University of Perugia in 1997. In 1999 she was Jemolo Fellow at the University of Oxford. Currently she is Affiliated Professor at the Umeå University. Her research focuses on Graphical Markov Models, both from the theoretical and applied view point. She has been working on identification of Graphical Markov Models with unobserved nodes, causal inference and bias induced by informative selection or drop-out. In 2020, she was Member of the Advisory Board of ISTAT for the design and implementation of sample surveys to measure and monitor epidemiological parameters of COVID-19 pandemic.Abstract
Self-selection into (or informative drop-out from) studies is a common problem in epidemiology, giving rise to a sampling bias that cannot be resolved by simply increasing the number of observations. Contributions exist that make use of graphical models where the selection mechanism is introduced explicitly as a node on which conditioning happens. We here build on the existing literature to extend the class of graphs for which the Average Treatment Effect, or other causal estimands, can be identified under selection. Sensitivity analysis against the identifying condition is also presented. If time permits, some analysis on Post/Long Covid Data coming from a Luigi Sacco University Hospital (LSUH) will also be presented.
Based on joint work with: Marco Doretti, Chris Caroni, Konstantina Gourgoura and Taiki Tezuka.
Schedule
- Friday 13 December 2024, 11:00-12:00 room seminar 1 @ Povo 0
Details
- Language: English
- For further information, please contact Prof. Veronica Vinciotti