Policy Learning with Network-Shaping Assignments

Many policies determine social arrangements whose effects depend on the relationships formed afterwards. We study how to learn such policies when a constrained joint action changes network formation and outcomes depend on exposures to the resulting network. Policy value integrates an outcome response over the distribution of post-assignment networks. The large number of feasible arrangements makes it difficult to evaluate policies directly from historical data. We therefore adopt a model-based approach that pools information across assignments to evaluate alternative arrangements. We characterize identification of policy values and construct plug-in estimators. Our regret bounds account for sampling variation, first-stage estimation error, and computational errors. Our applications to classroom seating and business groups illustrate predicted welfare gains from learned rules relative to random assignment.