  Causal analysis may be affected by selection bias, which is defined as the
systematic exclusion of data from a certain subpopulation. Previous work in
this area focused on the derivation of identifiability conditions. We propose
instead a first algorithm to address both identifiable and unidentifiable
queries. We prove that, in spite of the missingness induced by the selection
bias, the likelihood of the available data is unimodal. This enables us to use
the causal expectation-maximisation scheme to obtain the values of causal
queries in the identifiable case, and to compute bounds otherwise. Experiments
demonstrate the approach to be practically viable. Theoretical convergence
characterisations are provided.
