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Optimal policy learning under budget and coverage constraints: A Stata implementation

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  • Giovanni Cerulli

    (CNR-IRCrES)

Abstract

This presentation introduces opl_budget, a new community-contributed command for optimal policy learning under budget and minimum coverage constraints. Using estimated conditional average treatment effects (CATEs) and heterogeneous treatment costs, the command computes welfare-maximizing binary treatment assignment rules subject to a fixed budget and a minimum number of treated units. The command reports welfare gains, treatment coverage, and total policy costs and also allows evaluation of user-defined treatment rules for comparative policy analysis. An empirical example illustrates the use of the command in data-driven policy design and causal inference applications.

Suggested Citation

  • Giovanni Cerulli, "undated". "Optimal policy learning under budget and coverage constraints: A Stata implementation," UK Stata Conference 2026 16, Stata Users Group.
    • Handle: RePEc:boc:lsug26:16
    as

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