2022-06-28-guo22a.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 inference from observational datasets often relies on measuring and adjusting for covariates. In practice, measurements of the covariates can often be noisy and/or biased, or only measurements of their proxies may be available. Directly adjusting for these imperfect measurements of the covariates can lead to biased causal estimates. Moreover, without additional assumptions, the causal effects are not point-identifiable due to the noise in these measurements. To this end, we study the partial identification of causal effects given noisy covariates, under a user-specified assumption on the noise level. The key observation is that we can formulate the identification of the average treatment effects (ATE) as a robust optimization problem. This formulation leads to an efficient robust optimization algorithm that bounds the ATE with noisy covariates. We show that this robust optimization approach can extend a wide range of causal adjustment methods to perform partial identification, including backdoor adjustment, inverse propensity score weighting, double machine learning, and front door adjustment. Across synthetic and real datasets, we find that this approach provides ATE bounds with a higher coverage probability than existing methods.	First Conference on Causal Learning and Reasoning	Partial Identification with Noisy Covariates: A Robust Optimization Approach	2022	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	guo22a	0	Partial Identification with Noisy Covariates: A Robust Optimization Approach	318	335	318-335	318	false	Guo, Wenshuo and Yin, Mingzhang and Wang, Yixin and Jordan, Michael	given family Wenshuo Guo given family Mingzhang Yin given family Yixin Wang given family Michael Jordan	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/guo22a/guo22a.pdf