We consider the classical problem of decision-making using panel data, in which a decision-maker gets noisy, repeated measurements of multiple units (or agents). We consider a setup where there is a pre-intervention period, when the principal observes the outcomes of each unit, after which the principal uses these observations to assign a treatment to each unit. Unlike this classical setting, we permit the units generating the panel data to be strategic, i.e. units may modify their pre-intervention outcomes in order to receive a more desirable intervention. The principal's goal is to design a strategyproof intervention policy, i.e. a policy that assigns units to their correct interventions despite their potential strategizing. We first identify a necessary and sufficient condition under which a strategyproof intervention policy exists, and provide a strategyproof mechanism with a simple closed form when one does exist. Along the way, we prove impossibility results for strategic multiclass classification, which may be of independent interest. When there are two interventions, we establish that there always exists a strategyproof mechanism, and provide an algorithm for learning such a mechanism. For three or more interventions, we provide an algorithm for learning a strategyproof mechanism if there exists a sufficiently large gap in the principal's rewards between different interventions. Finally, we empirically evaluate our model using real-world panel data collected from product sales over 18 months. We find that our methods compare favorably to baselines which do not take strategic interactions into consideration, even in the presence of model misspecification.

翻译：我们考虑经典的面板数据决策问题，决策者可获得多个单元（或主体）的含噪重复观测值。我们设定一个干预前阶段，在该阶段中主体观测每个单元的结果，随后主体利用这些观测值为每个单元分配干预措施。与经典设置不同，我们允许生成面板数据的单元具有策略性，即单元可能修改干预前结果以获取更理想的干预。主体的目标是设计一种抗策略干预策略，即能在单元潜在策略行为下仍将其分配至正确干预措施的策略。我们首先识别出抗策略干预策略存在的充分必要条件，并给出当该条件满足时具有简单闭式解的抗策略机制。在此过程中，我们证明了策略性多分类问题的不可能性结果，该结果可能具有独立研究价值。对于两类干预情形，我们证明始终存在一种抗策略机制，并给出学习该机制的算法。对于三类及以上干预情形，我们提出一种算法：若主体在不同干预间的奖励存在足够大差距，则可学习到抗策略机制。最后，我们利用18个月产品销量真实面板数据对模型进行实证评估。研究发现，即便存在模型设定偏差，我们的方法仍优于未考虑策略交互的基线方法。