Treatment Allocation with Strategic Agents
研究了当个体会策略性地改变行为以获取更优处理时，如何设计最优分配规则，发现随机化分配可能更优，并提出了基于贝叶斯优化的序贯实验方法。
There is increasing interest in allocating treatments based on observed individual characteristics: examples include targeted marketing, individualized credit offers, and heterogeneous pricing. Treatment personalization introduces incentives for individuals to modify their behavior to obtain a better treatment. Strategic behavior shifts the joint distribution of covariates and potential outcomes. The optimal rule without strategic behavior allocates treatments only to those with a positive conditional average treatment effect. With strategic behavior, we show that the optimal rule can involve randomization, allocating treatments with less than 100% probability even to those who respond positively on average to the treatment. We propose a sequential experiment based on Bayesian optimization that converges to the optimal treatment rule without parametric assumptions on individual strategic behavior. This paper was accepted by Vivek Farias, data science. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2022.01629 .