Modern statistical and machine learning methods are increasingly capable of modeling individual or personalized treatment effects. These predictions could be used to allocate different interventions across populations based on individual characteristics. In many domains, like social services, the availability of different possible interventions can be severely resource limited. This paper 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, we use a counterfactual approach to show potential for substantial benefits in terms of reducing the number of families who experience repeat episodes of homelessness by choosing optimal allocations (based on predicted outcomes) to a fixed number of beds in different types of homelessness service facilities. Such changes in the allocation mechanism would not be without tradeoffs, however; a significant fraction of households are predicted to have a higher probability of re-entry in the optimal allocation than in the original one. We discuss the efficiency, equity and fairness issues that arise and consider potential implications for policy.

The United Nations described the Syrian conflict as the worst man-made disaster since World War II. We adopted a global perspective in examining the impact of the Syrian conflict on Syrians' physical, mental, and social well-being using the Gallup World Poll. Face-to-face interview data of 11,452 Syrian participants from 2008 to 2015 show that Syrians' physical (e.g., access to shelter), mental (e.g., life satisfaction), and social (e.g., social support) wellbeing decline substantially. Syrians who reported being exposed to the conflict are similarly affected compared to those without direct exposure, suggesting country-wide spillover effects. Global data covering 1.7 million participants across 163 countries from 2006 to 2016 show during the conflict, Syria's precipitous decline in well-being is unparalleled in the world, even when compared to countries similarly experiencing war, protests, and disasters. Our findings reinforce the vital importance of an accelerated peace process to restore wellbeing in Syria.

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Objective The study tests a community- and data-driven approach to homelessness prevention. Federal policies call for efficient and equitable local responses to homelessness. However, the overwhelming demand for limited homeless assistance is challenging without empirically supported decision-making tools and raises questions of whom to serve with scarce resources. Materials and Methods System-wide administrative records capture the delivery of an array of homeless services (prevention, shelter, short-term housing, supportive housing) and whether households reenter the system within 2 years. Counterfactual machine learning identifies which service most likely prevents reentry for each household. Based on community input, predictions are aggregated for subpopulations of interest (race/ethnicity, gender, families, youth, and health conditions) to generate transparent prioritization rules for whom to serve first. Simulations of households entering the system during the study period evaluate whether reallocating services based on prioritization rules compared with services-as-usual. Results Homelessness prevention benefited households who could access it, while differential effects exist for homeless households that partially align with community interests. Households with comorbid health conditions avoid homelessness most when provided longer-term supportive housing, and families with children fare best in short-term rentals. No additional differential effects existed for intersectional subgroups. Prioritization rules reduce community-wide homelessness in simulations. Moreover, prioritization mitigated observed reentry disparities for female and unaccompanied youth without excluding Black and families with children. Discussion Leveraging administrative records with machine learning supplements local decision-making and enables ongoing evaluation of data- and equity-driven homeless services. Conclusions Community- and data-driven prioritization rules more equitably target scarce homeless resources.