Policy design in experiments with unknown interference
The research develops experimental designs for policies with unknown internal cluster interactions and negligible external spillovers. It introduces single-wave and multiple-wave designs to estimate marginal policy effects, including spillover impacts. The work provides strong theoretical regret guarantees, ensuring that adaptive learning algorithms converge efficiently to optimal treatment rules in these complex environments.