There is growing concern about governments’ use of algorithms to make high-stakes decisions. While an early wave of research focused on algorithms that predict risk to allocate punishment and suspicion, a newer wave of research studies algorithms that predict “need” or “benefit” to target beneficial resources, such as ranking those experiencing homelessness by their need for housing. The present paper argues that existing research on the role of algorithms in social policy could benefit from a counterfactual perspective that asks: given that a social service bureaucracy needs to make some decision about whom to help, what status quo prioritization method would algorithms replace? While a large body of research contrasts human versus algorithmic decision-making, social service bureaucracies target help not by giving street-level bureaucrats full discretion. Instead, they primarily target help through pre-algorithmic, rule-based methods. In this paper, we outline social policy’s current status quo method—categorical prioritization—where decision-makers manually (1) decide which attributes of help seekers should give those help seekers priority, (2) simplify any continuous measures of need into categories (e.g., household income falls below a threshold), and (3) manually choose the decision rules that map categories to priority levels. We draw on novel data and quantitative and qualitative social science methods to outline categorical prioritization in two case studies of United States social policy: waitlists for scarce housing vouchers and K-12 school finance formulas. We outline three main differences between categorical and algorithmic prioritization: is the basis for prioritization formalized; what role does power play in prioritization; and are decision rules for priority manually chosen or inductively derived from a predictive model. Concluding, we show how the counterfactual perspective underscores both the understudied costs of categorical prioritization in social policy and the understudied potential of predictive algorithms to narrow inequalities.