论文标题
用于个人治疗效果估计的转移学习
Transfer Learning for Individual Treatment Effect Estimation
论文作者
论文摘要
这项工作考虑了在单个治疗效果（ITE）估计任务之间转移因果知识的问题。为此，我们从理论上评估了转移ITE知识的可行性，并提出了有效转移的实用框架。在目标任务的ITE误差上引入了一个下限，以证明由于缺乏反事实信息，ITE知识转移是具有挑战性的。然而，我们在目标任务的反事实损失和ITE误差上建立了上限，这证明了ITE知识转移的可行性。随后，我们引入了一个具有新的因果推理任务亲和力（CITA）的框架，以实现ITE知识转移。具体来说，我们使用CITA来找到最接近目标任务的源任务，并将其用于ITE知识转移。提供了经验研究，证明了该方法的功效。我们观察到，ITE知识转移可以显着（高达95％）减少ITE估计所需的数据量。
This work considers the problem of transferring causal knowledge between tasks for Individual Treatment Effect (ITE) estimation. To this end, we theoretically assess the feasibility of transferring ITE knowledge and present a practical framework for efficient transfer. A lower bound is introduced on the ITE error of the target task to demonstrate that ITE knowledge transfer is challenging due to the absence of counterfactual information. Nevertheless, we establish generalization upper bounds on the counterfactual loss and ITE error of the target task, demonstrating the feasibility of ITE knowledge transfer. Subsequently, we introduce a framework with a new Causal Inference Task Affinity (CITA) measure for ITE knowledge transfer. Specifically, we use CITA to find the closest source task to the target task and utilize it for ITE knowledge transfer. Empirical studies are provided, demonstrating the efficacy of the proposed method. We observe that ITE knowledge transfer can significantly (up to 95%) reduce the amount of data required for ITE estimation.
