Local Constraint-Based Causal Discovery under Selection Bias
This addresses the challenge of causal discovery under selection bias for researchers in fields like bioinformatics, though it appears incremental as it builds on existing FCI algorithm limitations.
The paper tackled the problem of discovering causal relations from independence constraints when selection bias is present, showing that Y-Structure patterns can soundly predict causal relations in this setup. They introduced a finite-sample scoring rule for Y-Structures that successfully predicted causal relations in simulations and performed well on real-world microarray data.
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.