2022-06-28-lowe22a.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
On time-series data, most causal discovery methods fit a new model whenever they encounter samples from a new underlying causal graph. However, these samples often share relevant information which is lost when following this approach. Specifically, different samples may share the dynamics which describe the effects of their causal relations. We propose Amortized Causal Discovery, a novel framework that leverages such shared dynamics to learn to infer causal relations from time-series data. This enables us to train a single, amortized model that infers causal relations across samples with different underlying causal graphs, and thus leverages the shared dynamics information. We demonstrate experimentally that this approach, implemented as a variational model, leads to significant improvements in causal discovery performance, and show how it can be extended to perform well under added noise and hidden confounding.	First Conference on Causal Learning and Reasoning	Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series Data	2022	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	lowe22a	0	Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series Data	509	525	509-525	509	false	L{\"o}we, Sindy and Madras, David and Zemel, Richard and Welling, Max	given family Sindy Löwe given family David Madras given family Richard Zemel given family Max Welling	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/lowe22a/lowe22a.pdf