2022-06-28-uemura22a.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
Understanding causal relations of systems is a fundamental problem in science. The study of causal discovery aims to infer the underlying causal structure from uncontrolled observational samples. One major approach is to assume that causal structures follow structural equation models (SEMs), such as the additive noise model (ANM) and the post-nonlinear (PNL) model, and to identify these causal structures by estimating the SEMs. Although the PNL model is the most general SEM for causal discovery, its estimation method has not been well-developed except for the bivariate case. In this paper, we propose a new causal discovery method based on the multivariate PNL model. We extend the bivariate method to estimate multi-cause PNL models and combine it with the iterative sink search scheme used for the ANM. We apply the proposed method to synthetic and real-world causal discovery problems and show its effectiveness.	First Conference on Causal Learning and Reasoning	A Multivariate Causal Discovery based on Post-Nonlinear Model	2022	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	uemura22a	0	A Multivariate Causal Discovery based on Post-Nonlinear Model	826	839	826-839	826	false	Uemura, Kento and Takagi, Takuya and Takayuki, Kambayashi and Yoshida, Hiroyuki and Shimizu, Shohei	given family Kento Uemura given family Takuya Takagi given family Kambayashi Takayuki given family Hiroyuki Yoshida given family Shohei Shimizu	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/uemura22a/uemura22a.pdf