Learning Resource Allocation Policies from Observational Data with an Application to Homeless Services Delivery. (arXiv:2201.10053v1 [cs.LG])

Web: http://arxiv.org/abs/2201.10053

We study the problem of learning, from observational data, fair and
interpretable policies that effectively match heterogeneous individuals to
scarce resources of different types. We model this problem as a multi-class
multi-server queuing system where both individuals and resources arrive
stochastically over time. Each individual, upon arrival, is assigned to a queue
where they wait to be matched to a resource. The resources are assigned in a
first come first served (FCFS) fashion according to an eligibility structure
that encodes …

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