2024-04-18-ahuja24a.md

title	software	abstract	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
Multi-Domain Causal Representation Learning via Weak Distributional Invariances	https://github.com/facebookresearch/MD-CRL	Causal representation learning has emerged as the center of action in causal machine learning research. In particular, multi-domain datasets present a natural opportunity for showcasing the advantages of causal representation learning over standard unsupervised representation learning. While recent works have taken crucial steps towards learning causal representations, they often lack applicability to multi-domain datasets due to over-simplifying assumptions about the data; e.g. each domain comes from a different single-node perfect intervention. In this work, we relax these assumptions and capitalize on the following observation: there often exists a subset of latents whose certain distributional properties (e.g., support, variance) remain stable across domains; this property holds when, for example, each domain comes from a multi-node imperfect intervention. Leveraging this observation, we show that autoencoders that incorporate such invariances can provably identify the stable set of latents from the rest across different settings.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	ahuja24a	0	Multi-Domain Causal Representation Learning via Weak Distributional Invariances	865	873	865-873	865	false	Ahuja, Kartik and Mansouri, Amin and Wang, Yixin	given family Kartik Ahuja given family Amin Mansouri given family Yixin Wang	2024-04-18		Proceedings of The 27th International Conference on Artificial Intelligence and Statistics	238	inproceedings	date-parts 2024 4 18	https://proceedings.mlr.press/v238/ahuja24a/ahuja24a.pdf