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Allocating Interventions Based On Counterfactual Predictions: A Case Study On Homelessness Services, Amanda R. Kube

McKelvey School of Engineering Theses & Dissertations

Modern statistical and machine learning methods are increasingly capable of modeling individual or personalized treatment effects by predicting counterfactual outcomes. These counterfactual predictions could be used to allocate different interventions across populations based on individual characteristics. In many domains, like social services, the availability of possible interventions can be severely resource limited. This thesis considers possible improvements to the allocation of such services in the context of homelessness service provision in a major metropolitan area. Using data from the homeless system, I show potential for substantial predicted benefits in terms of reducing the number of families who experience repeat episodes …