Individual treatment effect (ITE) estimation requires adjusting for the covariate shift between populations with different treatments, and deep representation learning has shown great promise in learning a balanced representation of covariates. However the existing methods mostly consider the scenario of binary treatments. In this paper, we consider the more practical and challenging scenario in which the treatment is a continuous variable (e.g. dosage of a medication), and we address the two main challenges of this setup. We propose the adversarial counterfactual regression network (ACFR) that adversarially minimizes the representation imbalance in terms of KL divergence, and also maintains the impact of the treatment value on the outcome prediction by leveraging an attention mechanism. Theoretically we demonstrate that ACFR objective function is grounded in an upper bound on counterfactual outcome prediction error. Our experimental evaluation on semi-synthetic datasets demonstrates the empirical superiority of ACFR over a range of state-of-the-art methods.

翻译：个体治疗效果（ITE）估计需要调整不同处理组别之间的协变量偏移，深度表征学习在学习协变量的平衡表征方面展现出巨大潜力。然而现有方法主要考虑二元处理场景。本文研究了更具实用性和挑战性的连续变量处理场景（例如药物剂量），并解决了该场景下的两个主要挑战。我们提出了对抗性反事实回归网络（ACFR），该网络以KL散度为目标通过对抗方式最小化表征不平衡，同时利用注意力机制维持处理值对结果预测的影响。理论上我们证明了ACFR目标函数建立在反事实结果预测误差的上界之上。在半合成数据集上的实验评估表明，ACFR在性能上优于一系列最先进方法。