2022-06-28-ahsan22a.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 reasoning in relational domains is fundamental to studying real-world social phenomena in which individual units can influence each other’s traits and behavior. Dynamics between interconnected units can be represented as an instantiation of a relational causal model; however, causal reasoning over such instantiation requires additional templating assumptions that capture feedback loops of influence. Previous research has developed lifted representations to address the relational nature of such dynamics but has strictly required that the representation has no cycles. To facilitate cycles in relational representation and learning, we introduce relational $\sigma$-separation, a new criterion for understanding relational systems with feedback loops. We also introduce a new lifted representation, $\sigma$-\textit{abstract ground graph} which helps with abstracting statistical independence relations in all possible instantiations of the cyclic relational model. We show the necessary and sufficient conditions for the completeness of $\sigma$-AGG and that relational $\sigma$-separation is sound and complete in the presence of one or more cycles with arbitrary length. To the best of our knowledge, this is the first work on representation of and reasoning with cyclic relational causal models.	First Conference on Causal Learning and Reasoning	Relational Causal Models with Cycles: Representation and Reasoning	2022	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	ahsan22a	0	Relational Causal Models with Cycles: Representation and Reasoning	1	18	1-18	1	false	Ahsan, Ragib and Arbour, David and Zheleva, Elena	given family Ragib Ahsan given family David Arbour given family Elena Zheleva	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/ahsan22a/ahsan22a.pdf