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Bayesian sensitivity analysis in causal analysis

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If you have a question about this talk, please contact Qingyuan Zhao.

Causal inference is based on the assumption of “conditional exchangeability”. This is not verifiable based on the data when using nonparametric modelling. A ``sensititvity analysis’’ considers the effect of deviations from the assumption. In a Bayesian framework, we could put a prior 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.

(Based on joint with St├ęphanie van der Pas and Bart Eggen.)

This talk is part of the Statistics series.

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