提出新方法估算连续治疗下的个体反应曲线，处理观测数据中的隐藏混杂因素。
Estimating Individual Dose-Response Curves under Unobserved Confounders from Observational Data
- 用带倾斜高斯先验的变分自编码器建模隐藏混杂因素为隐变量
- 在半真实数据集上相比现有方法误差降低最高达62%
- 适用于医疗、社会科学中需个性化治疗响应评估的场景
估计个体对连续变化治疗的潜在反应对于解决从医疗到社会科学中的因果问题至关重要。然而，现有方法要么仅限于二元治疗的因果效应估计，要么要求所有混杂变量均可测量。本文提出ContiVAE，一种新的框架，用于在存在未观测混杂因素的情况下，基于观测数据估计连续治疗的因果效应，即个体剂量-反应曲线。通过使用带有倾斜高斯先验的变分自编码器，ContiVAE将隐藏混杂因素建模为隐变量，能够预测每个个体在任意治疗水平下的潜在结果，并有效捕捉个体间的异质性。在半真实数据集上的实验表明，ContiVAE相较于现有方法性能提升最高达62%，展现出良好的鲁棒性和灵活性。在真实世界数据集上的应用也验证了其实际价值。
原文摘要 · Abstract (English)
Estimating an individual's potential response to continuously varied treatments is crucial for addressing causal questions across diverse domains, from healthcare to social sciences. However, existing methods are limited either to estimating causal effects of binary treatments, or scenarios where all confounding variables are measurable. In this work, we present ContiVAE, a novel framework for estimating causal effects of continuous treatments, measured by individual dose-response curves, considering the presence of unobserved confounders using observational data. Leveraging a variational auto-encoder with a Tilted Gaussian prior distribution, ContiVAE models the hidden confounders as latent variables, and is able to predict the potential outcome of any treatment level for each individual while effectively capture the heterogeneity among individuals. Experiments on semi-synthetic datasets show that ContiVAE outperforms existing methods by up to 62%, demonstrating its robustness and flexibility. Application on a real-world dataset illustrates its practical utility.
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