发表机构
Shanghai University of International Business and Economics（上海对外经贸大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对无未测量混杂假设难以满足的问题，提出PCL-U框架，利用神经编码器从观测协变量中自动学习代理变量，通过极小极大互信息目标和矩风险函数估计因果效应，在基准和合成数据上表现稳定且优于现有方法。
AI 中文摘要
从观测数据估计治疗效果通常依赖于无未测量混杂假设（NUCA），但该假设在实践中很少成立。近端因果学习（PCL）通过代理变量解决未测量混杂问题，然而现有方法要求代理变量预先指定。为此，我们提出PCL-U，一种直接从观测协变量中学习代理变量的框架。PCL-U使用神经编码器将协变量分解为治疗诱导、结果诱导和共享代理变量，并以极小极大互信息目标为指导，通过实用的基于矩的风险函数获得因果估计。基准实验表明，PCL-U匹配或优于现有基线。此外，两类具有不同维度和混杂强度的合成数据集表明，我们的方法保持稳定的估计精度。
英文摘要
Estimating treatment effects from observational data typically relies on the No Unmeasured Confounding Assumption (NUCA), which rarely holds in practice. Proximal causal learning (PCL) addresses unmeasured confounding via proxy variables, yet existing methods require the proxy variables to be pre-specified. Thus, we propose PCL-U, a framework that learns proxy variables directly from observed covariates. PCL-U uses neural encoders to decompose covariates into treatment-inducing, outcome-inducing, and shared proxies, guided by minimax mutual information objectives, and obtains causal estimates through a practical moment-based risk function. Experiments on benchmarks show that PCL-U matches or outperforms existing baselines. Besides, there are two types of synthetic datasets with varying dimensions and confounding strengths that illustrate that our method maintains stable estimation accuracy.
Comments14 pages,4 figures
