2022-06-28-zeng22a.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
Discovery of causal relationships from observational data, especially from mixed data that consist of both continuous and discrete variables, is a fundamental yet challenging problem. Traditional methods focus on polishing the data type processing policy, which may lose data information. Compared with such methods, the constraint-based and score-based methods for mixed data derive certain conditional independence tests or score functions from the data’s characteristics. However, they may return the Markov equivalence class due to the lack of identifiability guarantees, which may limit their applicability or hinder their interpretability of causal graphs. Thus, in this paper, based on the structural causal models of continuous and discrete variables, we provide sufficient identifiability conditions in bivariate as well as multivariate cases. We show that if the data follow our proposed restricted Linear Mixed causal model (LiM), such a model is identifiable. In addition, we proposed a two-step hybrid method to discover the causal structure for mixed data. Experiments on both synthetic and real-world data empirically demonstrate the identifiability and efficacy of our proposed LiM model.	First Conference on Causal Learning and Reasoning	Causal Discovery for Linear Mixed Data	2022	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	zeng22a	0	Causal Discovery for Linear Mixed Data	994	1009	994-1009	994	false	Zeng, Yan and Shimizu, Shohei and Matsui, Hidetoshi and Sun, Fuchun	given family Yan Zeng given family Shohei Shimizu given family Hidetoshi Matsui given family Fuchun Sun	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/zeng22a/zeng22a.pdf