2021-07-01-arbour21a.md

title	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
Permutation Weighting	A commonly applied approach for estimating causal effects from observational data is to apply weights which render treatments independent of observed pre-treatment covariates. Recently emphasis has been placed on deriving balancing weights which explicitly target this independence condition. In this work we introduce permutation weighting, a method for estimating balancing weights using a standard binary classifier (regardless of cardinality of treatment). A large class of probabilistic classifiers may be used in this method; the choice of loss for the classifier implies the particular definition of balance. We bound bias and variance in terms of the excess risk of the classifier, show that these disappear asymptotically, and demonstrate that our classification problem directly minimizes imbalance. Additionally, hyper-parameter tuning and model selection can be performed with standard cross-validation methods. Empirical evaluations indicate that permutation weighting provides favorable performance in comparison to existing methods.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	arbour21a	0	Permutation Weighting	331	341	331-341	331	false	Arbour, David and Dimmery, Drew and Sondhi, Arjun	given family David Arbour given family Drew Dimmery given family Arjun Sondhi	2021-07-01		Proceedings of the 38th International Conference on Machine Learning	139	inproceedings	date-parts 2021 7 1	http://proceedings.mlr.press/v139/arbour21a/arbour21a.pdf	label link Supplementary PDF http://proceedings.mlr.press/v139/arbour21a/arbour21a-supp.pdf