2023-04-11-assaad23a.md

title	software	abstract	section	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
Root Cause Identification for Collective Anomalies in Time Series given an Acyclic Summary Causal Graph with Loops	https://github.com/ckassaad/EasyRCA	This paper presents an approach for identifying the root causes of collective anomalies given observational time series and an acyclic summary causal graph which depicts an abstraction of causal relations present in a dynamic system at its normal regime. The paper first shows how the problem of root cause identification can be divided into many independent subproblems by grouping related anomalies using d-separation. Further, it shows how, under this setting, some root causes can be found directly from the graph and from the time of appearance of anomalies. Finally, it shows, how the rest of the root causes can be found by comparing direct effects in the normal and in the anomalous regime. To this end, an adjustment set for identifying direct effects is introduced. Extensive experiments conducted on both simulated and real-world datasets demonstrate the effectiveness of the proposed method.	Regular Papers	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	assaad23a	0	Root Cause Identification for Collective Anomalies in Time Series given an Acyclic Summary Causal Graph with Loops	8395	8404	8395-8404	8395	false	Assaad, Charles K. and Ez-Zejjari, Imad and Zan, Lei	given family Charles K. Assaad given family Imad Ez-Zejjari given family Lei Zan	2023-04-11		Proceedings of The 26th International Conference on Artificial Intelligence and Statistics	206	inproceedings	date-parts 2023 4 11	https://proceedings.mlr.press/v206/assaad23a/assaad23a.pdf