发表机构
University of Wisconsin–Madison（威斯康星大学麦迪逊分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对连续处理因果效应估计中的混杂与误设问题，提出WSENet，结合协变量平衡、样条扩展与加权正则化，实现稳健且优于基线的剂量反应建模。
AI 中文摘要
在观察性研究中，使用连续处理变量估计因果效应因混杂、模型误设和高维协变量而具有挑战性。我们提出了加权样条扩展网络（WSENet），这是一个端到端的神经框架，通过结合协变量平衡、结构化处理嵌入和偏差校正的结果估计来应对这些挑战。WSENet首先应用距离协变量最优权重，在不依赖参数模型的情况下诱导协变量与处理之间的分布独立性。然后，它通过一个结构化网络学习条件结果，该网络将结果相关的协变量表示与样条扩展的处理输入融合，从而实现对剂量-反应关系的平滑且灵活的建模。为了减轻残余偏差，我们引入了加权目标正则化，这是一种基于有效影响函数的校正技术，可产生双重稳健估计器。在半合成和真实世界数据集（包括高维基因组和环境健康数据）上的广泛评估表明，WSENet在准确性和稳定性方面始终优于现有基线。
英文摘要
Estimating causal effects with continuous treatments in observational studies is challenging due to confounding, model misspecification, and high-dimensional covariates. We propose the Weighted Spline-Expanded Network (WSENet), an end-to-end neural framework that addresses these challenges by combining covariate balancing, structured treatment embedding, and bias-corrected outcome estimation. WSENet first applies Distance Covariate Optimal Weights to induce distributional independence between covariates and treatment without relying on parametric models. It then learns the conditional outcome via a structured network that fuses outcome-relevant representations of covariates with a spline-expanded treatment input, enabling smooth and flexible modeling of the dose-response relationship. To mitigate residual bias, we introduce Weighted Targeted Regularization, a correction technique based on efficient influence functions that yields a doubly robust estimator. Extensive evaluations on semi-synthetic and real-world datasets, including high-dimensional genomic and environmental health data, demonstrate that WSENet consistently outperforms existing baselines in both accuracy and stability.
