2024-09-12-clivio24a.md

title	abstract	openreview	software	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
Towards Representation Learning for Weighting Problems in Design-Based Causal Inference	Reweighting a distribution to minimize a distance to a target distribution is a powerful and flexible strategy for estimating a wide range of causal effects, but can be challenging in practice because optimal weights typically depend on knowledge of the underlying data generating process. In this paper, we focus on design-based weights, which do not incorporate outcome information; prominent examples include prospective cohort studies, survey weighting, and the weighting portion of augmented weighting estimators. In such applications, we explore the central role of representation learning in finding desirable weights in practice. Unlike the common approach of assuming a well-specified representation, we highlight the error due to the choice of a representation and outline a general framework for finding suitable representations that minimize this error. Building on recent work that combines balancing weights and neural networks, we propose an end-to-end estimation procedure that learns a flexible representation, while retaining promising theoretical properties. We show that this approach is competitive in a range of common causal inference tasks.	KNgBCZXJkY	https://github.com/oscarclivio/representations_weighting	Papers	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	clivio24a	0	Towards Representation Learning for Weighting Problems in Design-Based Causal Inference	856	880	856-880	856	false	Clivio, Oscar and Feller, Avi and Holmes, Chris	given family Oscar Clivio given family Avi Feller given family Chris Holmes	2024-09-12		Proceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence	244	inproceedings	date-parts 2024 9 12	https://raw.githubusercontent.com/mlresearch/v244/main/assets/clivio24a/clivio24a.pdf