  We consider the problem of discovering causal relations from independence
constraints selection bias in addition to confounding is present. While the
seminal FCI algorithm is sound and complete in this setup, no criterion for the
causal interpretation of its output under selection bias is presently known. We
focus instead on local patterns of independence relations, where we find no
sound method for only three variable that can include background knowledge.
Y-Structure patterns are shown to be sound in predicting causal relations from
data under selection bias, where cycles may be present. We introduce a
finite-sample scoring rule for Y-Structures that is shown to successfully
predict causal relations in simulation experiments that include selection
mechanisms. On real-world microarray data, we show that a Y-Structure variant
performs well across different datasets, potentially circumventing spurious
correlations due to selection bias.
