Local Constraint-Based Causal Discovery under Selection Bias

Philip Versteeg, Joris Mooij, Cheng Zhang
Proceedings of the First Conference on Causal Learning and Reasoning, PMLR 177:840-860, 2022.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v177-versteeg22a, title = {Local Constraint-Based Causal Discovery under Selection Bias}, author = {Versteeg, Philip and Mooij, Joris and Zhang, Cheng}, booktitle = {Proceedings of the First Conference on Causal Learning and Reasoning}, pages = {840--860}, year = {2022}, editor = {Schölkopf, Bernhard and Uhler, Caroline and Zhang, Kun}, volume = {177}, series = {Proceedings of Machine Learning Research}, month = {11--13 Apr}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v177/versteeg22a/versteeg22a.pdf}, url = {https://proceedings.mlr.press/v177/versteeg22a.html}, 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. } }
Endnote
%0 Conference Paper %T Local Constraint-Based Causal Discovery under Selection Bias %A Philip Versteeg %A Joris Mooij %A Cheng Zhang %B Proceedings of the First Conference on Causal Learning and Reasoning %C Proceedings of Machine Learning Research %D 2022 %E Bernhard Schölkopf %E Caroline Uhler %E Kun Zhang %F pmlr-v177-versteeg22a %I PMLR %P 840--860 %U https://proceedings.mlr.press/v177/versteeg22a.html %V 177 %X 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.
APA
Versteeg, P., Mooij, J. & Zhang, C.. (2022). Local Constraint-Based Causal Discovery under Selection Bias. Proceedings of the First Conference on Causal Learning and Reasoning, in Proceedings of Machine Learning Research 177:840-860 Available from https://proceedings.mlr.press/v177/versteeg22a.html.

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