2022-06-28-liu22a.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
When using instrumental variables for causal inference, it is common practice to apply specific exclusion criteria to the data prior to estimation. This exclusion, critical for study design, is often done in an ad hoc manner, informed by a priori hypotheses and domain knowledge. In this study, we frame exclusion as a data-driven estimation problem, and apply flexible machine learning methods to estimate the probability of a unit complying with the instrument. We demonstrate how excluding likely noncompliers can increase power while maintaining valid treatment effect estimates. We show the utility of our approach with a fuzzy regression discontinuity analysis of the effect of initial diabetes diagnosis on follow-up blood sugar levels. Data-driven exclusion criterion can help improve both power and external validity for various quasi-experimental settings.	First Conference on Causal Learning and Reasoning	Data-driven exclusion criteria for instrumental variable studies	2022	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	liu22a	0	Data-driven exclusion criteria for instrumental variable studies	485	508	485-508	485	false	Liu, Tony and Lawlor, Patrick and Ungar, Lyle and Kording, Konrad	given family Tony Liu given family Patrick Lawlor given family Lyle Ungar given family Konrad Kording	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/liu22a/liu22a.pdf