Although a growing number of political scientists are conducting randomized experiments, many of them only report the average treatment effects and do not systematically explore the variation in treatment effects across subpopulations. This is unfortunate from a scientific point of view because heterogeneous treatment effects can provide additional substantive insights. This current state of affairs is also problematic from a policy makers' perspective since such studies do not identify subgroups for which treatments are effective. In this paper, we propose a formal two-step framework that first identifies heterogeneous treatment effects from a randomized experiment and then uses this information to derive an optimal policy about which treatment should be given to whom. Our proposed method avoids the risk of false discoveries that are likely in post hoc subgroup analysis routinely conducted in the discipline. We discuss our methodology in the context of get-out-the-vote randomized field experiments and show how the proposed two-step framework can be applied in real-world settings.
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