@conference {1500061,
	title = {From Empirical Analysis to Public Policy: Evaluating Housing Systems for Homeless Youth },
	booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)},
	year = {2018},
	abstract = {There are nearly 2 million homeless youth in the United
States each year. Coordinated entry systems are being used to provide
homeless youth with housing assistance across the nation. Despite these
efforts, the number of youth still homeless or unstably housed remains
very high. Motivated by this fact, we initiate a first study to understand and analyze the current governmental housing systems for homeless youth. In this paper, we aim to provide answers to the following questions: (1) What is the current governmental housing system for assigning
homeless youth to different housing assistance? (2) Can we infer the current assignment guidelines of the local housing communities? (3) What
is the result and outcome of the current assignment process? (4) Can we
predict whether the youth will be homeless after receiving the housing
assistance? To answer these questions, we first provide an overview of the
current housing systems. Next, we use simple and interpretable machine
learning tools to infer the decision rules of the local communities and
evaluate the outcomes of such assignment. We then determine whether
the vulnerability features/rubrics can be used to predict youth{\textquoteright}s homelessness status after receiving housing assistance. Finally, we discuss the
policy recommendations from our study for the local co},
	author = {Chan, Hau and Eric Rice and Vayanos, Phebe and Tambe, Milind and Morton, Matthew}
}
