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SUMMARY:Bayesian sensitivity analysis in causal analysis - Aad van der Vaa
 rt (TU Delft)
DTSTART:20230505T130000Z
DTEND:20230505T140000Z
UID:TALK199483@talks.cam.ac.uk
CONTACT:Qingyuan Zhao
DESCRIPTION:Causal inference is based on the assumption of "conditional ex
 changeability". This is not verifiable based on the data when using nonpar
 ametric modelling. A ``sensititvity analysis'' considers the effect of dev
 iations from the assumption. In a Bayesian framework\, we could put a prio
 r on the size of the deviation and obtain an ordinary posterior. We review
  possible approaches and present some results comparing different ways of 
 nonparametric modelling. \n\n(Based on joint with Stéphanie van der Pas a
 nd Bart Eggen.)
LOCATION:MR12\, Centre for Mathematical Sciences
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