Understanding individual treatment effects in extreme regimes is important for characterizing risks associated with different interventions. This is hindered by the fact that extreme regime data may be hard to collect, as it is scarcely observed in practice. In addressing this issue, we propose a new framework for estimating the individual treatment effect in extreme regimes (ITE$_2$). Specifically, we quantify this effect by the changes in the tail decay rates of potential outcomes in the presence or absence of the treatment. Subsequently, we establish conditions under which ITE$_2$ may be calculated and develop algorithms for its computation. We demonstrate the efficacy of our proposed method on various synthetic and semi-synthetic datasets.

翻译：理解极端条件中的个体处理效应对于评估不同干预措施相关的风险具有重要意义。然而，由于极端条件数据在实际中鲜少被观测到，其采集难度较大，这一问题阻碍了相关研究。为解决这一难题，我们提出了一种估计极端条件中个体处理效应（ITE$_2$）的新框架。具体而言，我们通过存在或不存在处理时潜在结果尾部衰减速率的变化来量化该效应。随后，我们建立了可计算ITE$_2$的条件，并开发了相应的计算算法。我们在多种合成及半合成数据集上验证了所提方法的有效性。