2022-06-28-versteeg22a.md

abstract	booktitle	title	year	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
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.	First Conference on Causal Learning and Reasoning	Local Constraint-Based Causal Discovery under Selection Bias	2022	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	versteeg22a	0	Local Constraint-Based Causal Discovery under Selection Bias	840	860	840-860	840	false	Versteeg, Philip and Mooij, Joris and Zhang, Cheng	given family Philip Versteeg given family Joris Mooij given family Cheng Zhang	2022-06-28		Proceedings of the First Conference on Causal Learning and Reasoning	177	inproceedings	date-parts 2022 6 28	https://proceedings.mlr.press/v177/versteeg22a/versteeg22a.pdf