2022-06-28-nabi22a.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
Recently there has been sustained interest in modifying prediction algorithms to satisfy fairness constraints. These constraints are typically complex nonlinear functionals of the observed data distribution. Focusing on the path-specific causal constraints, we introduce new theoretical results and optimization techniques to make model training easier and more accurate. Specifically, we show how to reparameterize the observed data likelihood such that fairness constraints correspond directly to parameters that appear in the likelihood, transforming a complex constrained optimization objective into a simple optimization problem with box constraints. We also exploit methods from empirical likelihood theory in statistics to improve predictive performance by constraining baseline covariates, without requiring parametric models. We combine the merits of both proposals to optimize a hybrid reparameterized likelihood. The techniques presented here should be applicable more broadly to fair prediction proposals that impose constraints on predictive models.	First Conference on Causal Learning and Reasoning	Optimal Training of Fair Predictive Models	2022	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	nabi22a	0	Optimal Training of Fair Predictive Models	594	617	594-617	594	false	Nabi, Razieh and Malinsky, Daniel and Shpitser, Ilya	given family Razieh Nabi given family Daniel Malinsky given family Ilya Shpitser	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/nabi22a/nabi22a.pdf