Continuous treatment effect estimation holds significant practical importance across various decision-making and assessment domains, such as healthcare and the military. However, current methods for estimating dose-response curves hinge on balancing the entire representation by treating all covariates as confounding variables. Although various approaches disentangle covariates into different factors for treatment effect estimation, they are confined to binary treatment settings. Moreover, observational data are often tainted with non-causal noise information that is imperceptible to the human. Hence, in this paper, we propose a novel Dose-Response curve estimator via Variational AutoEncoder (DRVAE) disentangled covariates representation. Our model is dedicated to disentangling covariates into instrumental factors, confounding factors, adjustment factors, and external noise factors, thereby facilitating the estimation of treatment effects under continuous treatment settings by balancing the disentangled confounding factors. Extensive results on synthetic and semi-synthetic datasets demonstrate that our model outperforms the current state-of-the-art methods.

翻译：连续处理效应估计在医疗保健和军事等各类决策与评估领域具有重要的实际意义。然而，当前用于估计剂量-反应曲线的方法依赖于将所有协变量视为混杂变量来平衡整个表征。尽管已有多种方法将协变量解耦为不同因子以进行处理效应估计，但这些方法仅限于二元处理场景。此外，观测数据常混杂着人类难以察觉的非因果噪声信息。因此，本文提出一种新颖的基于变分自编码器（DRVAE）的解耦协变量表征剂量-反应曲线估计器。我们的模型致力于将协变量解耦为工具因子、混杂因子、调整因子和外部噪声因子，从而通过平衡解耦后的混杂因子来促进连续处理设置下的处理效应估计。在合成与半合成数据集上的大量实验结果表明，我们的模型性能优于当前最先进的方法。