We consider the 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 utility-maximizing 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：我们研究基于面板数据进行决策的问题，即决策者获取多个单元（或代理人）含噪声的重复测量数据。考虑如下设定：在干预前阶段，主理人观测每个单元的结果，随后利用这些观测值为每个单元分配干预措施。与传统设定不同，我们允许生成面板数据的单元具有策略性，即单元可能为获得更优干预而调整其干预前结果。主理人的目标是设计一个策略性干预策略，即能够将单元分配至其效用最大化干预措施（即便单元可能进行策略性操控）的策略。我们首先给出存在策略性干预策略的充要条件，并在条件成立时提供具有简单闭式解的策略性机制。在此过程中，我们证明了策略性多分类的不可能性结论，该结果可能具有独立研究价值。当存在两种干预措施时，我们证明始终存在策略性机制，并提供学习该机制的算法。对于三种或以上干预措施，若主理人在不同干预措施间的奖励差异足够大，我们提供学习策略性机制的算法。最后，我们利用来自18个月产品销量数据的真实面板数据对模型进行实证评估。结果表明，即使存在模型设定偏误，我们的方法相比未考虑策略性互动的基准方法仍具有显著优势。