2022-06-28-faria22a.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
Recent work has shown promising results in causal discovery by leveraging interventional data with gradient-based methods, even when the intervened variables are unknown. However, previous work assumes that the correspondence between samples and interventions is known, which is often unrealistic. We envision a scenario with an extensive dataset sampled from multiple intervention distributions and one observation distribution, but where we do not know which distribution originated each sample and how the intervention affected the system, \textit{i.e.}, interventions are entirely latent. We propose a method based on neural networks and variational inference that addresses this scenario by framing it as learning a shared causal graph among a infinite mixture (under a Dirichlet process prior) of intervention structural causal models . Experiments with synthetic and real data show that our approach and its semi-supervised variant are able to discover causal relations in this challenging scenario.	First Conference on Causal Learning and Reasoning	Differentiable Causal Discovery Under Latent Interventions	2022	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	faria22a	0	Differentiable Causal Discovery Under Latent Interventions	253	274	253-274	253	false	Faria, Gon{\c{c}}alo Rui Alves and Martins, Andre and Figueiredo, Mario A. T.	given family Gonçalo Rui Alves Faria given family Andre Martins given family Mario A. T. Figueiredo	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/faria22a/faria22a.pdf