Local Constraint-Based Causal Discovery under Selection Bias

Philip Versteeg, Joris Mooij, Cheng Zhang

Keywords: causal discovery, causal inference, observational and experimental data, selection bias

TL;DR: We examine local constraint-based causal discovery methods under selection bias and confounding.

Abstract: 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.

Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/arxiv:2203.01848/code)