2022-06-28-brouillard22a.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
Causal discovery from observational data is a challenging task that can only be solved up to a set of equivalent solutions, called an equivalence class. Such classes, which are often large in size, encode uncertainties about the orientation of some edges in the causal graph. In this work, we propose a new set of assumptions that constrain possible causal relationships based on the nature of variables, thus circumscribing the equivalence class. Namely, we introduce typed directed acyclic graphs, in which variable types are used to determine the validity of causal relationships. We demonstrate, both theoretically and empirically, that the proposed assumptions can result in significant gains in the identification of the causal graph. We also propose causal discovery algorithms that make use of these assumptions and demonstrate their benefits on simulated and pseudo-real data.	First Conference on Causal Learning and Reasoning	Typing assumptions improve identification in causal discovery	2022	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	brouillard22a	0	Typing assumptions improve identification in causal discovery	162	177	162-177	162	false	BROUILLARD, PHILIPPE and Taslakian, Perouz and Lacoste, Alexandre and Lachapelle, Sebastien and Drouin, Alexandre	given family PHILIPPE BROUILLARD given family Perouz Taslakian given family Alexandre Lacoste given family Sebastien Lachapelle given family Alexandre Drouin	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/brouillard22a/brouillard22a.pdf