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  1. ; ; (, ACM)
    A key challenge in the design of effective anti-poverty programs is determining who should be eligible for program benefits. In devel- oping countries, one of the most common criteria is a Proxy Means Test — a simple decision rule that determines eligibility based on basic information about each household (for example, the number of rooms in the household, the number of children, whether there is indoor plumbing, and other observable characteristics) [1, 3, 4, 7]. At the core of each Proxy Means Test (PMT) is a machine learning algorithm that uses the short list of household characteristics to pre- dict whether the household should be deemed poor, and therefore eligible, or non-poor, and therefore ineligible [5, 6]. 
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