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 Stéphanie van der Pas and Bart Eggen.)